analysis of security in blockchain case study in 51 attack detecting

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 11 January 2024

Blockchain security enhancement: an approach towards hybrid consensus algorithms and machine learning techniques

• K. Venkatesan 1 , 2 &
• Syarifah Bahiyah Rahayu 1   nAff2  

Scientific Reports volume  14 , Article number:  1149 ( 2024 ) Cite this article

5757 Accesses

10 Citations

Metrics details

• Engineering
• Mathematics and computing

In this paper, we propose hybrid consensus algorithms that combine machine learning (ML) techniques to address the challenges and vulnerabilities in blockchain networks. Consensus Protocols make ensuring agreement among the applicants in the distributed systems difficult. However, existing mechanisms are more vulnerable to cyber-attacks. Previous studies extensively explore the influence of cyber attacks and highlight the necessity for effective preventive measures. This research presents the integration of ML techniques with the proposed hybrid consensus algorithms and advantages over predicting cyber-attacks, anomaly detection, and feature extraction. Our hybrid approaches leverage and optimize the proposed consensus protocols' security, trust, and robustness. However, this research also explores the various ML techniques with hybrid consensus algorithms, such as Delegated Proof of Stake Work (DPoSW), Proof of Stake and Work (PoSW), Proof of CASBFT (PoCASBFT), Delegated Byzantine Proof of Stake (DBPoS) for security enhancement and intelligent decision making in consensus protocols. Here, we also demonstrate the effectiveness of the proposed methodology within the decentralized networks using the ProximaX blockchain platform. This study shows that the proposed research framework is an energy-efficient mechanism that maintains security and adapts to dynamic conditions. It also integrates privacy-enhancing features, robust consensus mechanisms, and ML approaches to detect and prevent security threats. Furthermore, the practical implementation of these ML-based hybrid consensus models faces significant challenges, such as scalability, latency, throughput, resource requirements, and potential adversarial attacks. These challenges must be addressed to ensure the successful implementation of the blockchain network for real-world scenarios.

Similar content being viewed by others

An enhanced consensus algorithm for blockchain

Deep learning personalized recommendation-based construction method of hybrid blockchain model

An improved PBFT consensus algorithm based on grouping and credit grading

Introduction.

The Consensus protocols employed in the blockchain network provide high security and more efficient operations. Hybrid consensus algorithms are developed by combining the key elements of various consensus algorithms. This might be useful to prevent double-spending and 51% of attacks. Combining Proof of Work (PoW) and Delegated Proof of Stake (DPoS) improves computation performance and enhances high security 1 . DPoS is used for block validation, and PoW is used for block creation, making it more difficult for an attacker to control the network. The combination of Proof of Stake (PoS) and Proof of Work (PoW) results in better security performance along with the network's decentralization 2 . PoS is used for block validation, and PoW is used for block creation, increasing the network's security and decentralization 3 . Integrating the DPoS and Practical Byzantine Fault Tolerance (PBFT) provides higher security, scalability, and efficiency. Here, DPoS is used for block creation, and PBFT is used for block validation, providing better security and scalability 4 .

Hybridization of the Casper and PBFT consensus algorithms can provide a higher level of security against 51% of attacks in blockchain technology. Casper Algorithm adopts PoS for block validation 5 . Here, validators are selected based on their network stake size. PBFT works differently compared to other algorithms. It confides a group of validators to grasp the following block consensus. Hybridization of PoS and PBFT leverages high-level security and rapid consensus time. This makes it very difficult for attackers to access the network and perform cyber-attacks. In addition, consensus hybridization balances scalability and decentralization, which is convenient for microgrid networks 6 . For any algorithm, achieving a complete security audit and system update, which confirms the efficacy, is essential. Compared to other individual consensus algorithms, these hybridization algorithms enhance security by preventing 51% of attacks 7 . However, analyzing the trade-offs before implementing them in blockchain networks is mandatory. Figure  1 shows the basic block diagram using the blockchain network and ML techniques.

Basic block diagram using blockchain and ML techniques.

Many researchers propose a new consensus protocol that performs better security and scalability and reduces the probability of cyber-attacks on the blockchain network. In 8 , the author proposed Algorand, which achieves transaction finality and high scalability 9 introduces consensus algorithms, which enhance throughput, scalability, and security. The authors Zhu 10 introduced the GHOST protocol, the modified PoW consensus protocol, which achieves the best security and high throughput transaction. Meanwhile 11 , A. Kiayias et al. propose ouroboros, the modified PoS protocol that enhances scalability and security. Lashkari et al. 12 eliminate 51% of attacks using Bitcoin-NG, improving throughput and scalability. These works accentuate the necessity of creating new consensus protocols that perform highly scalable security and have the potential to handle vast volumes of transactions.

The Hybrid consensus algorithms with Machine Learning (ML) techniques gained significant approaches, which led to high performance, improved scalability, and enhanced security in blockchain networks. Several researchers, such as Zhang 13 , Andoni 14 , Wang 15 , and Yang 16 , conduct various simulations and experiments and discusses hybrid protocols, which evaluate their efficacy and perform better results and security than the existing consensus mechanisms. Additionally, Mahmood et al. 17 propose a comprehensive review of consensus protocols that enlighten the potential of ML approaches for enhancing performance and security. The author highlights the hybrid consensus mechanism and ML integration to achieve optimal block validation and improve network performance.

Over the years, the performance of the hybrid consensus protocol combined with Particle Swarm Optimization (PSO) has been studied. In 18 , Zhu et al. propose a PoS-based consensus model with PSO that achieves high scalability, better performance, and enhanced security. Razali et al. 19 Introduce a new consensus model, which uses PSO for block validation optimization. Ali et al. 20 Work on hybrid consensus protocol with PSO approaches, which results in high security and scalability and maintains high performance. Ullah et al. 21 propose ML-based PSO for faster block validation and better throughput. Kumar et al. 22 Provide a comparative study of existing consensus protocol and highlight the potential of the hybrid consensus protocol with PSO optimization techniques. It also demonstrates the strength of hybridization and achieves better performance and scalability, enlightening future research requirements in blockchain networks.

Unsupervised, supervised, and rule-based ML approaches 23 are vital in responding to and detecting microgrid attacks in blockchain networks. ML techniques may also be used to solve communication and behavior-based attacks. ML-based hybrid consensus algorithms improve security and other performance factors and provide a scope for active research 24 . Specifically, new consensus protocols with ML approaches detect and prevent the significant threats of attacks in blockchain networks. These papers propose simulation models and demonstrate experiments that can perform better security than any other consensus mechanisms 25 . Additionally, these papers provide a comprehensive overview, realize the potential of hybridization, and highlight the importance of further research to develop more effective and efficient methods to secure blockchain mechanisms 26 .

However, the researchers propose a diverse solution, develop a creative approach, and offer a state-of-the-art framework to overcome cybersecurity issues. In 27 , the author proposes Fed-Inforce-Fusion, which is a reinforcement learning-based fusion model for the Internet of Medical Things (IoMT) networks. This method incorporates federated and reinforcement learning to improve accuracy and detection. Meanwhile, the "PC-IDS" framework uses hybrid machine learning approaches to identify harmful behaviors and secure privacy in cyber-physical power networks 28 . However, deep autoencoder IDS performs well and accurately in real-time intrusion detection in IIoT networks 29 .

Additionally, the multi-stage AV framework combines the state-of-the-art framework and deep learning techniques, outperforming existing systems in understanding and identifying cyber risks within autonomous vehicles 30 . Therefore, the relevance of these studies lies in their exploration of hybrid consensus algorithms and integration of machine learning approaches, offering valuable insights into blockchain and cyber security attacks. Despite their contributions, several limitations were prevalent in the studies reviewed. These limitations underscore the need for further research to address these gaps and refine our understanding of integrating machine-learning approaches in hybridizing consensus algorithms.

Open challenges and motivation

Consensus algorithms face several open challenges that need to be addressed for the widespread adoption of blockchain technology in real-world applications. One of the primary challenges is scalability , as consensus mechanisms must efficiently handle many transactions per second without compromising security and decentralization. Another critical challenge is energy efficiency , particularly in Proof of Work (PoW), where the high energy consumption is unsustainable and costly in the long run. Developing energy-efficient consensus algorithms or improving existing ones is vital for practical applications. Ensuring fast transaction confirmation times is another challenge to meeting the demands of real-time applications. Long confirmation times can hinder the usability of blockchain technology, making it necessary to optimize latency and transaction confirmation. Security is an ongoing concern, and consensus mechanisms must resist attacks such as double-spending, Sybil, and 51% attacks. Enhancing security measures is crucial for building trust and widespread adoption.

Governance and compliance are significant challenges, as consensus algorithms must align with legal and regulatory requirements while maintaining decentralization. Finding the right balance between compliance and decentralization is crucial for the finance, healthcare, and supply chain management industries. Interoperability between blockchain networks and consensus algorithms is essential for collaboration and communication across systems. Developing standards and protocols for seamless integration and data exchange is challenging. Privacy and confidentiality are paramount, and consensus algorithms must incorporate robust techniques to protect sensitive data while maintaining transparency and audibility. Consensus mechanisms should adapt to dynamic networks , where nodes can join or leave anytime and handle challenges such as node churn, network partitions, and malicious nodes. Improving the user experience is vital for widespread adoption, requiring consensus algorithms to minimize transaction fees, reduce latency, and provide a seamless and intuitive interface.

Addressing the environmental impact of consensus algorithms, particularly energy-intensive ones like PoW, is a pressing challenge. Developing sustainable and eco-friendly consensus algorithms is essential to align blockchain technology with global sustainability goals. Overcoming these challenges will require continuous research, innovation, and collaboration between industry, academia, and regulatory bodies. By addressing these open challenges, consensus algorithms can pave the way for the widespread adoption of blockchain technology in various real-world applications.

Research gap identification

The analysis of existing literature reveals a distinct gap in integrating hybrid consensus algorithms with machine learning approaches. While previous studies have laid essential groundwork, there remains unexplored territory in understanding the concepts of hybridization and difficulties in integrating machine learning approaches to enhance the security of the blockchain network. This research strives to fill this gap by defining the research objectives, thereby advancing our understanding of improving security in blockchain networks and protecting the network from malicious attacks.

Research objectives

The main goal of this research paper is to implement a hybrid consensus mechanism with ML techniques, which enhances the security of the consensus mechanisms and avoids cyber attacks. The contribution of this research is listed below.

To identify and understand the vulnerabilities in existing mechanisms. Here, analyzing and evaluating the security shortcomings enables a comprehensive understanding of the vectors and potential threats.

To reframe the hybrid consensus Algorithms. A comprehensive analysis of consensus algorithm hybridization and its necessity in cyber security is discussed here.

To develop the ML framework for extraction of the features and anomaly detection. The critical aspects of the ML framework that effectively performs feature extraction, anomalies, and malicious activity detection within the blockchain network must be performed. This framework will leverage advanced ML algorithms to analyze network behavior, identify suspicious patterns, and distinguish normal activities from potential attacks.

To integrate the ML framework with consensus mechanisms. The developed ML framework will be integrated into the consensus algorithms discussed in this work to enhance the security of consensus mechanisms. This integration will enable real-time monitoring and proactive defense mechanisms against attacks, thereby ensuring the integrity and stability of blockchain networks.

To evaluate the effectiveness of the hybrid approach. The proposed hybrid ML approach can be thoroughly evaluated. The evaluation would be focused on security enhancements achieved through the proposed solution, which leads the system to an adoptive selection of consensus algorithms and intelligent decision-making.

By addressing these challenges, this research paper aims to contribute to blockchain security by proposing novel consensus algorithms and hybrid ML approaches that enhance the overall security of blockchain networks and mitigate the risk of cyber-attacks.

The contribution of the proposed hybrid consensus algorithms is listed below.

This proposed hybridization combines the strength of different consensus mechanisms, mitigates the vulnerabilities, and enhances scalability.

The hybrid consensus algorithm uses energy-efficient mechanisms to reduce environmental impact without compromising security.

This model can adapt to dynamic conditions and confirms robustness.

This proposed method can integrate privacy-enhancing features and protect sensitive information.

Additionally, hybrid models can improve trust by providing robust and resilient consensus mechanisms.

The contributions of the ML techniques are listed below:

ML can greatly enhance threat detection capabilities and improve the trustworthiness of the network by providing a multilayer defense.

It improves the adaptability to emerging threats and acts as an intelligent layer to optimize consensus mechanisms based on network conditions and improve scalability challenges.

ML techniques can adjust the consensus mechanism based on energy availability and consumption patterns.

It also learns continuously from the network behavior and adjusts security measures to meet network dynamics.

Cryptographic techniques and advanced privacy-preserving algorithms can further enhance the confidentiality of transactions and user data.

Decentralized systems can detect and respond to security threats using machine learning to identify patterns and anomalies, reducing the risk of successful attacks.

Resource allocation can be optimized by dynamically assessing threat levels and adjusting security measures accordingly. This ensures efficient allocation of resources to areas with the highest risk.

The rest of the paper is organized as follows: Section " Background study " establishes the preliminaries for the challenges and vulnerabilities in existing consensus mechanisms. A detailed discussion of the previous research on 51% of attacks and their impact is also presented in this section. ML Techniques for Security Enhancement are discussed in Section " Machine learning techniques for security enhancement ". Section " Research methodology and materials " presents the research methodology and materials and its advantages and optimizations achieved through the proposed approach. The experimental implementations and results are discussed in Section " Security enhancements achieved through the proposed solution ". Section " Open issues and challenges of the hybrid consensus approach " presents the proposed hybrid consensus approaches' open challenges and future scope. The conclusions and future work of the paper are discussed in Section " Conclusion ".

Background study

Challenges and vulnerabilities in existing consensus mechanisms.

The progress in blockchain technology has played a crucial role in addressing challenges related to decentralization, security, and consensus formation using various consensus mechanisms. However, these mechanisms have their own set of challenges and vulnerabilities. This section will delve into the concerns and susceptibilities of existing consensus mechanisms.

Proof of work (PoW)

PoW is a consensus algorithm that exhibits several challenges and vulnerabilities. Firstly, PoW requires substantial computational power and energy consumption to solve complex cryptographic problems, resulting in high energy consumption that is environmentally unsustainable 31 . This energy-intensive nature raises concerns about the ecological impact of blockchain networks utilizing PoW. Secondly, scalability becomes a concern as the network grows, as the PoW algorithm needs help to maintain efficient consensus and transaction validation in the face of increasing participant numbers 32 . The computational requirements become increasingly demanding, potentially limiting the scalability potential of PoW-based blockchains.

Additionally, the concentration of mining power in a few large mining pools introduces concerns regarding mining centralization and the potential for collusion 33 . This centralization raises questions about the democratic nature of the blockchain network and the potential for malicious activities by a concentrated mining power 34 . The priority toward long-term sustainability and PoW-based blockchain adoptions are required to address the challenges.

Proof of stake (PoS)

The Proof-of-Stake (PoS) consensus protocols face vulnerabilities and challenges that include the possibility of wealth concentration and must be addressed within the network 35 . In PoS, validators are chosen based on the number of coins they have staked, meaning that those with more enormous stakes are more likely to be selected as validators. This wealth concentration can undermine the network's decentralization and compromise its security 6 . Another issue is the nothing at stake problem, where validators can vote on multiple forks simultaneously without consequences. Validators can create forks and change transaction history, which may lead to double-spending attacks 36 . To mitigate these challenges, solutions such as implementing penalties for malicious behavior, implementing robust governance mechanisms, and ensuring widespread participation can help maintain the security and integrity of the PoS network. Additionally, ongoing research and development efforts are needed to address these vulnerabilities and improve the overall robustness of the PoS consensus mechanism 37 . PoS has energy efficiency advantages, but its vulnerabilities must be acknowledged to ensure blockchain network security.

Delegated proof of stake (DPoS)

The DPoS algorithm is widely adopted for its effectiveness and scalability; however, it poses significant challenges and vulnerabilities. A primary concern is the potential for centralization, as DPoS relies on a small group of trusted delegates responsible for block production and validation 38 . This concentration of power introduces the risk of influential entities engaging in vote buying or collusion, compromising decentralization and fairness. Another area for improvement is low voter participation, leading to a lack of representation and potential governance problems 39 . In order to overcome these challenges, measures should be implemented to promote decentralization and encourage greater voter engagement. This includes preventing manipulation through mechanisms that safeguard the integrity of the election process 40 . Educating token holders about the importance of voting and its impact on network governance can increase participation. Introducing mechanisms for delegate rotation or limiting their terms can prevent long-term centralization 41 . Through continuous research and protocol improvements, DPoS can balance efficiency and decentralization, offering a more inclusive consensus mechanism for blockchain networks by enhancing transparency and promoting active participation 42 .

Practical byzantine fault tolerance (PBFT)

PBFT offers the advantage of withstanding Byzantine faults but also presents challenges and assumptions that must be addressed. Scalability is a limitation of PBFT, as the latency increases with more nodes due to the communication required for consensus 43 . This makes PBFT more suitable for smaller networks or consortium blockchains. The assumption of PBFT regarding the number of faulty nodes is critical, as it assumes that at most one-third of nodes are faulty. When a higher proportion of nodes become malicious or inaccurate, PBFT's ability to maintain consensus can be compromised 44 . However, researchers have dedicated their efforts to strengthening the scalability and resilience of PBFT in order to overcome these challenges 45 . Optimizations like parallelization and batching have been proposed to reduce communication overhead and latency in more extensive networks. Fault-tolerant algorithms and Byzantine fault detection techniques aim to handle situations where the assumed threshold of faulty nodes is exceeded 46 . Hybrid consensus models combining PBFT with Proof of Stake or Proof of Work have also been explored to balance scalability and fault tolerance. Researchers have proposed various methods to overcome these challenges and improve the scalability, durability, and usability of PBFT in various blockchain networks 47 .

Proof of authority (PoA)

PoA relies on a predetermined set of approved validators responsible for validating and adding new blocks to the blockchain. While PoA offers certain benefits, it also poses notable limitations and vulnerabilities. A key concern is the potential for centralization 48 . However, carefully selecting validators and proactive measures to prevent infiltration can ensure continued decentralization and strengthen network security, leading to a robust and resilient system. The concentration of authority in the hands of a small group of validators contradicts the decentralization goal that blockchain technology aims to achieve 49 . Another challenge in PoA is the need for incentives for validators. Unlike Proof of Work and Proof of Stake, PoA does not provide incentives or rewards to validators, as block creation authority is based on reputation or identity rather than a commitment of resources. The absence of incentives will lead to lower participation and highly compromise the network's security 50 . Validators may become less vigilant or refrain from active participation in block validation, leading to a less secure and reliable network. Certain modifications have been proposed for PoA to address these challenges. For instance, implementing a reputation-based system or penalties for misbehavior can mitigate the risk of collusion among validators 51 .

Additionally, offering incentives in the form of transaction fees or token awards can promote active participation and ensure the stability and security of the network. The PoA system offers faster transactions and uses less energy. However, there are concerns about security and trust because it relies on a set of predetermined validators. Additionally, when implementing PoA, it is necessary to consider the balance between centralization and decentralization 50 .

Casper presents a more secure and reliable consensus mechanism, addressing challenges traditional proof-of-stake (PoS) algorithms face. However, a significant challenge in Casper lies in parameter selection. Careful consideration is required to balance security, liveness, and fault tolerance 52 . Improper parameter values can lead to vulnerabilities and compromise the protocol's effectiveness. Conservative parameters may hinder efficient block finalization, while permissive parameters can increase the risk of malicious behavior 53 . Thorough analysis and understanding of network dynamics and trade-offs are necessary to determine appropriate parameter values for Casper. Another critical aspect of Casper is using slashing conditions to penalize validators for malicious behavior 54 . However, defining and enforcing slashing conditions without introducing false positives or negatives is a complex challenge. False positives penalize honest validators wrongly, while false negatives allow malicious validators to escape penalties. Striking the right balance is crucial to prevent unfair penalization and ensure appropriate punishment 55 . Designing robust and accurate slashing conditions requires careful consideration and analysis to minimize false positives and negatives.

Researchers are enhancing Casper to address system challenges through better parameter selection and slashing conditions. This method involves rigorous empirical analysis, simulation studies, and formal verification techniques 56 . It also enables us to confidently determine the most influential parameter choices and optimize the settings for feasible results. Extensive testing and experimentation assess the impact and behavior of slashing conditions in real-world scenarios 57 . Future advancements may involve automated or adaptive parameter selection mechanisms that dynamically adjust based on network characteristics. Similarly, improvements in slashing conditions can be achieved through ML or incorporating external reputation systems. Continued research and development efforts will lead to more robust and practical implementations of Casper.

Previous research on cyber 51% of attacks and their impact

Previous research on 51% of attacks has explored the potential risks and consequences of these attacks in blockchain networks. By gaining control over 50% of the network's mining power, an attacker can manipulate the blockchain's transactions, potentially leading to double spending or other fraudulent activities . It is necessary to assimilate the importance of 51% of attacks for establishing adequate security and authenticity maintenance of the blockchain network.

Attack vector identification

This is a challenging research problem related to 51% of attacks. Several studies and experiments have been conducted to understand the various attack vectors. By analyzing these vectors, researchers can enhance security and improve countermeasures. Mining centralization is one of the prominent attacks. Studies are performed to examine the mining power concentration 58 . This study includes investigating the distribution of mining resources, examining the incentives for mining centralization, and identifying the potential risks associated with such centralization. Network partitioning usually occurs when we split the blockchain network into multiple subnets. These results from various technical issues, intentional attacks, and other network disruptions, which blockchain networks need to address 59 . However, researchers have analyzed the influence of network partitioning on consensus mechanisms and their vulnerabilities. After examining these problems, researchers aim to mitigate the issues connected with network partitioning and ensure blockchain integrity. Rent attacks acquire more computational power and control the hash rate's prominence, enabling attackers to manipulate the transaction. Researchers can examine the influence of this attack on other consensus mechanisms, which emphasize the importance of prevention protocols to avoid the presence 20 . However, researchers can also investigate the chance of group mining to launch the attack. When mining pools or entities collude with each other, they combine the computational power and control the hash rate, as discussed in the rental attack, and manipulate the transaction in the blockchain network 60 . This results in the importance of protection mechanisms to avoid and reduce collusion between the mining entities. Furthermore, researchers also analyzed these attacks to detect and mitigate the related vulnerabilities.

Double spending and transaction reversal

These attacks aim to achieve double-spending and transaction reversal, exploiting vulnerabilities in the transaction verification process. By controlling most of the network's mining power through a 51% attack, an attacker can manipulate the blockchain's transaction history, allowing them to spend the same coins multiple times 61 . Previous research has extensively explored the economic incentives and feasibility of double-spending attacks, considering factors such as attack cost, potential gains, and impact on the network's reputation 62 . Evaluating the economic viability of these attacks helps understand their motivations and enables countermeasures to be developed. Proposed countermeasures include increasing the number of confirmations required for transaction finality, implementing mechanisms for detecting suspicious transactions, and enhancing consensus algorithm security 63 . Advancements in blockchain technology have introduced additional measures to mitigate double-spending risks, such as faster block confirmation times and additional security layers like two-factor authentication and multi-signature transactions 64 . Research on the economic incentives and potential impact of double-spending attacks has led to the developing enhanced security protocols, promoting trust and reliability in cryptocurrency transactions.

Blockchain security and trust

Blockchain security and trust are critical considerations in designing and operating blockchain networks. The occurrence of 51% of attacks represents a significant threat to the security and trustworthiness of these networks. Extensive research has been conducted to assess the impact of such attacks on transaction integrity and overall reliability 65 . Successful attacks will compromise the accuracy and reliability of blockchain technology. Controlling network-mining power will lead attackers to manipulate the transaction history. This will cause double-spending attacks and confirms the earlier transactions 66 . This action will destroy the confidence of the user in the blockchain network. This will make users stumble on network transactions and cause fraudulent or fearful activities. However, the blockchain network always depends on user participation and adopting successful transactions, which causes economic repercussions 67 .

Earlier studies discussed the significant consequences of these attacks and recommended the importance of security measures to protect 68 . In addition, researchers are also focusing on mitigating and detecting 51% of attacks, which improves consensus mechanisms, decentralization governance, and enhancing network resilience 69 . Addressing the security and trust issues requires multi-faceted approaches. This approach involves technical solutions, regulatory measures, governance frameworks, and other industrial standards 70 . Research collaboration with policymakers, industrialists, and stakeholders ensures effective practices and security measures. Educating users and stakeholders will establish trust, comprehensive adoption, and usage.

Countermeasures and prevention

Hybrid consensus protocol combines PoW, PoS, and other mechanisms to improve security and avoid these attacks. These models will leverage the strengths of the approaches and mitigate the weaknesses. For instance, Hybrid PoW and PoS will improve security and reduce the mining centralization problem, which addresses the nothing at stake in PoS 71 . Likewise, hybrid PBFT leads to an increase in fault tolerance and scalability. These models can improve security and avoid 51% of attacks 72 .

Improving mining decentralization is also another preventive measure that has been proposed by researchers 73 . Promoting a distributed network of miners may lower the mining concentration power, which makes it more difficult for single or group entities to control the computational resources 74 . This technique can mitigate the problem of 51% of attacks through power distribution among various participants and confirms diverse and resilient networks. Introducing penalties for adverse behavior is vital to discouraging and avoiding 51% of attacks 75 . Consensus algorithms introduce various mechanisms that reduce the probability of 51% of attacks. However, improving network monitoring and malicious detections are essential to identify potential attack patterns, which trigger timely responses and avoid 51% of attacks. In machine learning, anomaly detection approaches are used to analyze the data and help detect suspicious activities that show the potential 51% of attacks 76 . Active network monitoring leads to the detection of attacks and ensures the security and integrity of the blockchain.

Impact on decentralization and consensus

The 51% of attacks can cause severe implications in the decentralization of the blockchain network. Hence, it should be considered as an immediate concern. Extensive research provides an understanding of the consequences of attacks on network governance, concentration power, and decision-making processes 77 . Decentralization shows that any single or group of entities does not control the network, immutability, resistance, and fostering transparency. However, a perfect 51% of attacks can allow an attacker to control the network and threaten decentralization. This power concentration contradicts decentralization, which introduces vulnerabilities and compromises the blockchain network's trust and integrity 78 . The existing consensus algorithms show the consensus reaching and validating the transactions . When a 51% attack occurs, the attacker can manipulate the consensus process, potentially invalidating transactions or reversing confirmed blocks. This disrupts the integrity of the consensus mechanism and raises concerns about the validity of the entire blockchain 79 . Research on the impact of 51% of attacks emphasizes the criticality of maintaining a decentralized network structure and robust consensus mechanisms. Efforts are directed toward developing countermeasures that promote decentralization and enhance the resilience of consensus protocols against such attacks 80 . Hybrid consensus models, for example, aim to combine multiple consensus algorithms to mitigate the vulnerabilities of individual approaches and achieve a more balanced and secure network. Safeguarding decentralization and consensus mechanisms also involves addressing factors such as governance and decision-making processes. Research explores ways to ensure fair and democratic governance structures where decision-making power is distributed among network participants 81 . Decentralized governance models prevent authority concentration with on-chain voting and transparent protocols.

In the real world, 51% of attacks serve as valuable resources for understanding the methodologies, impact, and responses associated with such attacks. Previous research has analyzed notable incidents, including attacks on Bitcoin Gold, Verge, and Ethereum Classic, to gain insights into the nature and consequences of these attacks 82 . By examining case studies, researchers can delve into attackers' specific techniques to gain majority control over the network's mining power. This analysis helps identify vulnerabilities within the consensus mechanisms and highlight areas where improvements are needed.

In recent years, many case studies have provided insights about the impacts of these attacks in the blockchain community, corresponding responses, and steps to reduce their effects 83 . Understanding the successful 51% of attacks can help to assess the economic losses, potential damage, and disintegration of user trust. This can be crucial for analyzing network participants to represent vulnerabilities, strengthen network security, evolve countermeasures, and provide active security strategies. Previous studies on 51% of attacks can provide the fundamentals to improve security, governance framework, developing mechanisms, monitoring, and detecting systems 84 . By gaining knowledge through these case studies, we can identify vulnerabilities, patterns, and best practices to develop more resilient blockchain networks. It is necessary to understand that the process of 51% of attacks can change frequently, leading to new attack vectors and the emergence of other techniques. Therefore, ongoing research and collaboration are essential to anticipate threats and address vulnerabilities associated with these attacks. The insights from case studies and real-world examples highlight the importance of continuous research efforts and interdisciplinary collaboration among researchers, developers, policymakers, and industry stakeholders. All these factors are combined to enhance security, trustworthiness, and resilience in the face of 51% attacks and other emerging threats to blockchain networks.

Consensus algorithms have several limitations that must be addressed to implement them effectively in real-world applications. Scalability is a significant concern, as many consensus algorithms need help to handle high transaction volumes and large network sizes. Finding efficient solutions to scale while maintaining security and decentralization is crucial for accommodating the demands of real-world applications. Energy efficiency is another limitation, especially in consensus algorithms like Proof of Work (PoW) that consume substantial energy. This not only makes them environmentally unfriendly but also economically unsustainable. Developing energy-efficient consensus algorithms is essential to reduce the carbon footprint associated with blockchain technology and ensure long-term viability.

Specific consensus algorithms introduce centralization risks, such as Proof of Stake (PoS) and Delegated Proof of Stake (DPoS). These algorithms can concentrate control in the hands of validators with more significant stakes or selected delegates, compromising the decentralization and trust that blockchain technology aims to provide. Balancing decentralization and stakeholder influence is crucial to maintaining a robust and inclusive network. Trust assumptions in consensus algorithms pose another limitation. Many algorithms rely on pre-approved validators or trusted authorities, which may not align with blockchain technology's decentralized and trustless nature. This limitation restricts the applicability of consensus algorithms in specific real-world applications that require higher levels of trust and security without relying on centralized entities.

Privacy and confidentiality are challenging within consensus algorithms prioritizing transparency and immutability. Striking a balance between data privacy and transparency is a complex task that consensus algorithms must address to protect sensitive information while maintaining the auditability and transparency required by various applications. Addressing these limitations in consensus algorithms will require continuous research, innovation, and collaboration between stakeholders in the blockchain community. By overcoming these challenges, consensus algorithms can unlock the full potential of blockchain technology in a wide range of real-world applications while ensuring scalability, energy efficiency, decentralization, trust, and privacy.

Machine learning techniques for security enhancement

Overview of machine learning algorithms applicable to blockchain security.

Machine learning algorithms offer a range of techniques that can be applied to enhance blockchain security. These algorithms leverage artificial intelligence and data analysis to detect anomalies, identify patterns, and make predictions, thereby strengthening the resilience of blockchain networks against potential security threats. This section discusses an overview of machine learning algorithms applicable to blockchain security.

Supervised learning algorithms

Supervised learning algorithms, such as Support Vector Machines (SVM) and Random Forests (RF), play a vital role in blockchain security by enabling classification tasks and bolstering the detection and prevention of fraudulent or malicious activities 85 . Support Vector Machines (SVM) stand out as a widely utilized supervised learning algorithm in the context of blockchain security. SVMs excel in binary classification tasks, where transactions must be categorized as legitimate or malicious. By creating a hyperplane that separates the two classes in a high-dimensional feature space, SVMs strive to find the optimal hyperplane that maximizes the separation of data points while minimizing classification errors. SVMs boast a robust theoretical foundation and are particularly effective in scenarios where the data is not linearly separable. Kernel functions manage the high-handling feature spaces more efficiently and achieve linear and non-linear classification tasks .

RF is an ensemble learning algorithm that utilizes multiple decision trees and performs predictions 86 . Each decision tree in this algorithm is trained on the data subset and uses random feature selections. The final predictions can be made by combining the individual tree predictions. RF has the potential to handle high-dimensional data and its robustness. This technique is effective in handling tasks that involve regression and classification. In blockchain networks, RF is highly effective in determining whether the transactions are legitimate or fraudulent through investigating features and patterns related to their security. RF can also detect malicious nodes by analyzing their interactions and behaviors.

Both SVM and RF offer distinct advantages in terms of performance and interpretability. SVMs are acclaimed for their adeptness in handling complex data and finding optimal decision boundaries, while RF excels in managing large and diverse datasets 24 . Both algorithms can deliver accurate and reliable results within blockchain security applications. It is important to note that the efficacy of these supervised learning algorithms relies on the quality and representativeness of the training data. Labeled datasets containing examples of legitimate and malicious transactions or nodes are crucial for effectively training the models 38 . Furthermore, feature engineering plays a critical role in extracting meaningful features from blockchain data, thereby enhancing the performance of these algorithms.

Unsupervised learning algorithms

Unsupervised learning algorithms such as clustering and anomaly detection will aid blockchain security by identifying threats and patterns without labeled training data. Clustering algorithms group similar transactions or network entities in blockchain security. Algorithms such as k-means or DBSCAN partition the data into clusters, each representing a group of similar data points 42 . Clustering algorithms can differentiate between regular and unusual behavior by examining the patterns in each cluster. This helps to identify security threats or suspicious activities in the blockchain network. This capability is precious when specific attacks or anomalies are unknown in advance, as clustering algorithms can unveil unknown patterns or groups within the data.

On the other hand, anomaly detection algorithms focus on identifying outliers or anomalies that significantly deviate from the expected behavior 87 . Algorithms such as Isolation Forest or One-Class SVM construct models of the expected behavior within the data and flag any instances that fall outside this norm. In blockchain security, anomaly detection algorithms can help identify unusual or suspicious transactions, network nodes, or activities that may indicate potential security threats, fraud, or network intrusions 85 . Identifying these irregularities will lead to rapid implementation of security measures, minimize the risk, and protect the blockchain network's dependability. Unsupervised learning can also provide valuable insights into the character and structure of blockchain data. This makes it feasible to detect the potential security risks and irregularities that may not have been previously labeled or identified. These algorithms can provide a comprehensive and proactive approach that enhances the capabilities of blockchain security. An unsupervised learning algorithm requires expert parametric tuning to perform precise and significant outcomes 84 . In cluster algorithm selection, several clusters or thresholds for anomaly detection can significantly influence the algorithm's efficiency. However, data preprocessing and feature engineering are essential in unsupervised learning that prepares the data, which relies on inherent structure and data distribution.

Deep learning algorithms

Deep learning algorithms such as CNNs and RNNs significantly influence blockchain security 88 . CNNs are highly effective in analyzing structured data like transaction graphs. Meanwhile, RNNs excel in analyzing sequential data like transaction histories and brilliant contract execution. These algorithms can effectively identify malicious and fraudulent activities by detecting anomalies and patterns. It can provide advantages such as autonomous learning, accurate anomaly detection, and handling large datasets.

Moreover, deep learning algorithms can continuously improve their performance, allowing them to adapt to new attack patterns and evolving security threats 89 . Nevertheless, it is crucial to consider that deep learning algorithms require substantial amounts of labeled training data and significant computational resources to train and deploy effectively. Model interpretability and explainability can be challenging with deep learning models, given their complex nature and operation as black boxes. Ensuring the privacy and security of sensitive blockchain data during the training process is also a critical consideration 90 . By integrating deep learning algorithms, such as CNNs and RNNs, into blockchain security frameworks, researchers can enhance threat detection and strengthen the trustworthiness and resilience of blockchain networks.

Reinforcement learning

Reinforcement Learning (RL) is a valuable branch of machine learning that enables the training of intelligent agents to make sequential decisions to maximize cumulative rewards 91 . RL algorithms like Q-learning or DQNs can help create intelligent agents to secure blockchain with optimal security policies. Blockchain agents boost security by verifying blocks, selecting consensus methods, and preventing attacks. Through interactions with the blockchain network, RL agents observe the current state, take actions, and receive rewards or penalties based on the outcomes of their activities, thereby learning from their experiences during training 92 . RL agents explore the environment during training and learn through trial and error. The goal is to obtain a policy that increases rewards over time by enhancing security measures and reducing potential threats to blockchain security. RL empowers blockchain networks with adaptive and intelligent decision-making capabilities. RL agents can learn to identify attack patterns, anticipate vulnerabilities, and respond to emerging threats in real-time 93 . This adaptability is particularly valuable in blockchain security's dynamic and evolving landscape, where new attack vectors and vulnerabilities continually occur.

Furthermore, RL algorithms can be combined with other techniques like supervised or unsupervised learning to enhance the learning process. Pre-training RL agents with previous data or labeled examples improves their learning speed and performance in new situations. However, deploying RL algorithms in blockchain security presents challenges 94 . One critical challenge involves defining an appropriate reward structure that accurately reflects the security objectives of the blockchain network. RL agents must balance exploring new security measures and relying on established ones by finding a balance between exploring new actions and exploiting known strategies.

Bayesian networks

This algorithm uses probabilistic reasoning, which helps the system model and analyze the elemental correlations between the variables and blockchain security. It shows uncertain and incomplete information with directed acyclic graphs 95 . The calculations are performed for the event probability based on their observed evidence by quantifying conditional variable dependencies and probabilities. The Bayesian network provides valuable information, such as a blockchain network's security risks and vulnerabilities, and enables stakeholders to act appropriately 96 . By incorporating additional communication and handling missing or incomplete data with probabilistic reasoning, this algorithm is flexible and quickly adapted to changing the real-world scenario.

Generative adversarial networks (GANs)

In a blockchain network, GANs are a vital tool to enhance security in distributed networks. It has two components, a generator, and a discriminator, that create artificial datasets that replicate the real-world data exactly. GANs provide several benefits, such as creating diverse, realistic data for attacking scenarios 97 . This approach can simulate the attackers' actions and improve the network's defense system. However, this model can also enhance the anomaly detection system, improving blockchain security . In order to train the GANs, enormous computation resources and extensive training data sets are required 98 . Furthermore, it requires high-quality and diverse datasets, which influence the reliability of the synthetic data and improve the performance. Even though it has limitations, implementing GANs in blockchain provides real-world opportunities for developing attacking scenarios, testing blockchain security, and performing an effective anomaly detection system.

Privacy-preserving techniques (PPTs)

This technique has two security components, namely Differential Privacy (DP) and Secure Multi-Party Computation (SMPC). These components are necessary for maintaining confidentiality and anonymity in the distributed blockchain network 99 . DP can enable control over the noise, data perturbation, and query response, which avoids data point identification. However, SMPC works on multiple parts that collaborate with ML tasks and computations and improve privacy. Combining these approaches, PPTs can ensure the safety of the applicant's data and compliance with data protection regulations. Furthermore, it can also enable the detection of anomalies and prevent malicious activities 100 . While selecting the appropriate methods, we should consider the requirements such as privacy level, data type, analysis or prediction task, and computational overhead.

Feature extraction and anomaly detection for 51% attack prevention

As a researcher, it is imperative to highlight the significance of feature extraction and anomaly detection in preventing 51% of attacks in blockchain networks. These techniques are pivotal in identifying and flagging abnormal patterns or behaviors that may signify potential attacks, allowing prompt intervention to mitigate associated risks. By delving into feature extraction and anomaly detection details, we can gain deeper insights into their crucial role in preventing 51% of attacks in blockchain networks.

Feature extraction

Analyzing blockchain data requires feature extraction, which is crucial in identifying and extracting meaningful information that captures the essential characteristics of transactions, network behavior, and participant activities 101 . Analysts obtain meaningful insights such as the blockchain network's security, performance, and efficiency, enabling information-based decision-making. Transaction-based features provide insights into individual transactions, encompassing transaction size, frequency, time stamp, input–output ratios, graph properties, and volumes 36 . Furthermore, these functions expedite the anomalies, detecting the malicious transactions and recognizing patterns associated with the malicious behavior.

The behavior, connectivity patterns, centrality, and consensus participation of blockchain are analyzed through network-based features. This analysis helps evaluate the network structure, identifies the significant nodes that influence it, and identifies any abnormal network activity 98 . Participant-based features center on individual participants within the blockchain network, encompassing reputation scores, stake sizes, and consensus participation history. These features assess the trustworthiness of participants, detect potential malicious actors, and evaluate the reliability of network contributors 102 . Smart contract-based features involve the analysis of smart contract code and properties, allowing vulnerability assessments, identification of attack vectors, and monitoring for suspicious behaviors during contract execution 59 .

Feature extraction is the foundation for subsequent analysis and modeling tasks in blockchain analysis. ML algorithms acquire the extracted features and perform statistical analyses. Researchers can gain insights that help to identify the pattern and make formal decisions based on these inputs. The feature selection always depends on the data under examination in blockchain and meets the goal, which helps the researchers better understand the blockchain network.

Anomaly detection

Detecting anomalies is critical, which secures the blockchain and maintains integrity. The process involves the identification of deviations that lead to potential attacks, abnormalities, and fraudulent activities. Irregularities can be identified using graph-based methods, behavioral analysis, ML, and statistical techniques 103 . Statistical approaches utilize statistical techniques to identify abnormalities in transaction patterns, network behavior, or consensus parameters. Clustering techniques group similar transactions or network entities to identify outliers or unusual patterns 98 . Outlier detection methods focus on identifying data points that significantly deviate from the expected distribution. Time-series analysis detects anomalies in temporal patterns or trends, enabling the identification of abnormal behaviors over time.

Machine learning approaches are impressive for anomaly detection in blockchain networks. Unsupervised learning algorithms automatically identify anomalies based on patterns and statistical deviations from normal behavior. These algorithms learn from previous data, detect subtle changes, and adapt to evolving attack patterns. Supervised learning algorithms can also be utilized with labeled data to classify instances as normal or anomalous based on known patterns 23 . Graph-based approaches leverage the structure of the blockchain network to identify anomalies. Graph analysis techniques detect changes in node centrality measures, unexpected or suspicious connections, or alterations in community structures. Analyzing the network graph can detect malicious activity like changes in consensus participation or new connections 36 .

The behavioral analysis focuses on establishing normal behavior profiles based on previous data. Analyzing transaction patterns, network interactions, or consensus participation can establish a baseline of expected behavior 104 . Deviations from these profiles, such as sudden changes in transaction volumes, irregular consensus participation, or unusual network interactions, are flagged as anomalies. Threshold-based approaches involve setting thresholds for specific features or behaviors and monitoring deviations beyond those thresholds. For example, exceeding a certain threshold in transaction volume or encountering a predetermined limit of consensus failures may indicate an anomaly 105 . These approaches are straightforward to implement and provide an early warning system for potential anomalies. By utilizing these approaches, researchers can enhance the security and robustness of blockchain networks by promptly detecting and mitigating anomalies.

Real-time monitoring and alerting

Real-time monitoring and alerting are integral components of a robust anomaly detection system in blockchain networks. The ability to detect anomalies in real-time and promptly respond to potential cyber-attacks is necessary for upholding network security and integrity 90 . Real-time monitoring ensures that any abnormal behavior or suspicious activities are swiftly identified and addressed, thereby minimizing the potential effect of attacks. Continuous analysis of blockchain data through ongoing anomaly detection methods is essential for real-time monitoring. As discussed previously, machine-learning models can be trained to detect anomalies in real-time and deployed to analyze incoming data streams for deviations from normal behavior or expected patterns 84 . The system uses machine-learning algorithms to identify and alert users of potential attacks or malicious activities as soon as they are detected. Automatic alerts are challenging to identify quickly and immediately respond to anomalies. This information can be sent through email, SMS, or mentioned on a dashboard. Integrating the ML model with the defined framework process can lead to real-time monitoring. Developing well-defined responding mechanisms involving active protocols isolating the compromised nodes and enabling additional security measures is necessitated 106 .

Dynamic adaptation

Dynamic techniques lead the blockchain network to adopt 51% of attacks that detect and respond to emerging threats. The continuous learning process in ML models can analyze the attacking pattern, detect new threats, prevent potential attacks, and improve accuracy 107 . This never-ending learning makes the system practical for identifying potential security risks. Feedback loops are critical for effective functioning. It can provide valuable information about false positives and negatives, improving anomaly detection accuracy, refining thresholds, and enhancing overall performance 100 . Dynamic adaption made the adjustments by consensus parameters or security measures and mitigated the potential attacks based on anomaly detection. Network configuration can be modified, limiting the action of malicious transactions or nodes and preventing possible attacks. Feature extraction is a critical factor in enabling dynamic adaptation and minimizing the effects of the attacks 108 . The system can identify the characteristics by extracting valuable information from blockchain transactions. This is more valuable to detect anomalies and identify deviations from the patterns. This technique will be updated based on new attacks and includes the latest indicators and behaviors related to 51% of attacks.

Machine learning-based consensus decision-making

ML-based consensus hybridization is a promising method that enhances blockchain networks' security and trust. ML Techniques such as data analysis and pattern recognition can improve the network mechanisms. Intelligent decision-making is performed by leveraging ML algorithms. By investigating the previous data, ML models can collect the essential information that predicts the effects of various consensus performance parameters. This leads to optimizing the consensus parameters such as block size, difficulty level, and adaption over the change in network and improving scalability. In addition, ML-based consensus decision-making commits to blockchain security by implementing the identification, detection, and mitigation of suspicious attacks. ML models monitor the consensus process, detect anomalies, perform more proactive measures, and serve the network's integrity. Furthermore, this method also attains the potential to recast the consensus approach toward data availability, interpretability, privacy, and governance. This section discusses the different factors and their influence on the ML-based consensus decision-making process.

Consensus parameter optimization

Consensus parametric optimization is required to develop a high-performance, secure, and scalable blockchain network. ML algorithms optimize these parameters using the previous data and real-time network conditions. These models can investigate the previous data, which helps to identify the patterns and trends of the network performances through block size, difficult adjustment, time, and validation rules. ML model can identify the different parameters affecting the network's performance and optimize values that improve the efficiency of blockchain operations. In real-time scenarios, monitoring networking conditions allows ML models to adopt the dynamic consensus parameters, which examines the network metrics and provides necessary modification over the consensus mechanisms. These models provide valid suggestions to increase the block size and decrease block time, enhancing scalability in higher transactions. However, when the network faces any security threats, the proposed model learns from experience and recommends changing the difficult level or other validation rules and adapting consensus parameters to improve security. The proposed model can analyze the previous data and understand how the parametric setting affects the network performance and behavior. This learning model will refine the proposed system, which will result in improving the accuracy and optimizing the performance. Through ML, optimizing consensus parameters can improve network performance and security while also improving blockchain systems' scalability and adaptability. ML models can adjust the consensus parameters as the network changes to fit the new conditions, workloads, and threat levels. Adaptability allows the blockchain network to maintain its efficiency, security, and resilience even when faced with dynamic and changing environments.

Adaptive consensus selection

Blockchain networks need a suitable consensus algorithm that adapts to their dynamic conditions and needs. By analyzing past data and network metrics, ML is vital in choosing the best consensus algorithm. ML models can analyze performance metrics of various consensus mechanisms, such as transaction throughput and confirmation latency. These analyses can identify patterns and trends that highlight the strengths and weaknesses of each algorithm in specific network environments. Real-time monitoring allows the machine learning models to make adaptive decisions regarding consensus algorithm selection. The models constantly analyze network load, security needs, and node capabilities to select the best algorithm for the current situation. For instance, during heavy traffic, the system may choose a consensus algorithm that increases transaction speed. However, if security is the top priority, a Byzantine fault-tolerant algorithm may be selected instead. The ML models learn from previous data and adapt to changing conditions, improving their decision-making accuracy. Adaptive consensus selection brings numerous benefits to blockchain networks. Our system carefully selects the best consensus algorithm for every situation to maximize network efficiency, ensuring top-notch performance and resource utilization. Another important aspect is that it boosts network security by adapting the consensus algorithm according to the current security needs and potential threats. The network can protect itself against attacks and preserve the integrity of the blockchain system by adjusting to the situation.

Intelligent block validation

Ensuring the safety and accuracy of a blockchain network relies heavily on intelligent block validation. ML models can play a crucial role in this process. ML algorithms can help to make informed decisions about block validation by analyzing transaction data, network behavior, and consensus rules. By analyzing validated blocks from the past, these algorithms can detect patterns that signify valid transactions. This helps them develop a thorough understanding of a legitimate transaction. ML models categorize the valid and invalid blocks, improving block validation. This procedure can reduce manual inspection and detect malicious activities, improving efficiency in data transactions and network behavior. ML models can correctly identify potential threats, which leads to learning and evolving from the newly arrived data. This introduces effective adaption, changes the network conditions, and elevates themselves from attacking strategies. Furthermore, intelligent block validation improves security and confirms the valid blocks added to the blockchain.

Fraud detection and prevention

Blockchain networks can utilize the ML model and sophisticated algorithms, which analyze transaction patterns and network behavior and detect and prevent malicious activities effectively. The ML model analyzes previous data and detects unusual patterns and behaviors identified as fraudulent activity precisely and accurately . This makes the ML tools more powerful to maintain security and integrity. The proposed system uses supervised learning approaches and trains on labeled data to accurately classify whether the transactions are valid. This method also considers parameters such as transaction amount, time stamps, network interaction, and user behavior for exact predictions. Unsupervised learning methods can analyze the transaction pattern and network behavior, identify deviations from the normal ones, and finally detect anomalies in the blockchain data. This method detects fraud and unknown practices more effectively and identifies the double-spending or Sybil attacks as fraud attempts .

Furthermore, Natural language processing (NLP) techniques are used to analyze the textual data, feedback, and forum discussions to detect potential vulnerabilities. Keeping ML algorithms up-to-date and learning continuously from the new data will always recognize the patterns and prevent fraudulent activities. Earlier detection can reduce potential damage and enable high security. Hybridization of ML and Blockchain develops the network into more trustworthy and resilient.

Predictive analytics for consensus optimization

Predictive analytics are essential and enhance consensus mechanisms. ML models can analyze previous data and patterns, forecast network conditions, and optimize consensus processes. This model goes through different situations and predicts the network behavior by examining network latency, resource usage, and transaction throughput. This leads to the proposed model's ability to perform proactive, intelligent decision-making, which optimizes the consensus mechanisms. Predictive analysis can offer intelligent parametric optimization and adaptive consensus selection, which is anticipated to mitigate security threats. This allows the proposed system design to perform efficient and adaptable operations of the blockchain network under dynamic workloads and varying environments. Predictive analytics is useful for detecting and preventing fraud. Machine learning models can identify patterns of fraudulent activity, which can then trigger alerts and preventive measures for participants in the network. This proactive approach helps maintain the blockchain network's integrity and trustworthiness.

Research methodology and materials

This section will discuss the proposed architecture's steps, components, and modules. The proposed architecture is developed using the ProximaX blockchain infrastructure platform that combines blockchain with the distributed service layers. This integrates blockchain networks with decentralized storage, database, streaming, and enhanced smart contract services to create an all-in-one user-friendly platform. ProximaX is designed to achieve high scalability, and throughput provides low latency. This platform is available in private, public, and consortium configurations and accommodates additional services without compromising the performance. This unique platform is built on reliable technologies that can be used in all industry segments. Researchers and practitioners can easily design and develop an application on a secure with high availability at a low cost.

The proposed architecture combines ML approaches with consensus to achieve agreement in a blockchain network. Integrating ML algorithms with consensus mechanisms can significantly enhance decision-making in distributed systems. These hybrid algorithms aim to address the limitations of consensus protocols by utilizing ML models. The algorithm gathers important data, extracts meaningful features, and trains ML models with data from the system model. These models are then incorporated into the consensus protocol to optimize decision-making, improve security through anomaly detection, and enable adaptive learning. Figure  2 provides a visual representation of how ML models are integrated with hybrid consensus algorithms in a ProximaX blockchain network to enhance efficiency, scalability, and fault tolerance while ensuring the integrity of the consensus process through the prediction model.

Proposed system using blockchain and machine learning layer.

Initially, the module is developed for collecting and extracting the necessary information from the ProximaX blockchain network. Next, the feature extraction module processes the data to extract meaningful features that capture pertinent information for consensus. The ML training module then uses various algorithms to train models based on the extracted features. The anomaly detection module analyzes incoming data using the trained ML models to identify abnormal behaviors or attacks. If any anomalies or attacks are detected, the consensus decision-making module evaluates them, assesses their impact, and determines appropriate actions to maintain consensus integrity. Finally, the consensus enforcement module ensures that the decisions made by the consensus decision-making module are enforced within the network. This iterative process involves continuous feedback, where data is collected, features are extracted, models are trained, anomalies and attacks are detected, consensus decisions are made, and enforcement is carried out. This enables the consensus architecture to adapt to changing network conditions, identify anomalies or attacks, and maintain consensus integrity based on intelligent ML decision-making. Figure  3 shows the design flow of the proposed work. The detailed discussion over the methodology involved in this research is discussed below through step-by-step analysis.

Design flow diagram for the proposed work.

Review and identify the attack scenarios : The first step is reviewing existing consensus mechanisms and analyzing their limitations in required applications. Additionally, it identifies the different attacks that can occur in an application using blockchain. This information can be used to create a set of labeled data that can be used to train the machine learning algorithms.

Choose a consensus algorithm : The second step is to choose an appropriate consensus algorithm that meets the specific requirements of the blockchain-based system. Here, the ProximaX-based blockchain environment is considered. Hybrid consensus algorithms, which combine elements of different consensus algorithms, can provide robustness and security to the blockchain system. This can help prevent cyber-attacks, such as 51% of attacks and double-spending attacks. This algorithm will ensure the integrity and reliability of the blockchain. Some of the hybridizations of consensus algorithms discussed in this research work are listed below.

Delegated proof of stake work (DPoSW) : DPoS validates blocks in the blockchain while PoW creates them, making it harder for attackers to manipulate the network. It uses a limited number of elected validators, enabling faster block confirmation times and higher transaction throughput than PoW. However, DPoS comes with the risk of centralization and relies on trust in elected representatives, which can compromise decentralization. These factors should be considered when evaluating consensus algorithms.

Proof of stake and work (PoSW) : Blockchain uses PoS and PoW to enhance security and efficiency. PoW increases security against attacks, while PoS allows for better energy efficiency and scalability. However, PoS can lead to power concentration, requiring mechanisms to address the "nothing at stake" problem. Balancing decentralization and efficiency involves trade-offs. PoS creates governance complexity and requires careful management for transparency and inclusivity. Long-term security is a consideration as reliance on PoS increases and PoW decreases.

Proof of CASBFT : Casper-PBFT is a hybrid consensus algorithm that combines Proof of Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT) to improve network security and transaction speed. It offers strong consistency, rapid transaction finality, and scalability advantages. However, careful selection and governance of validators are necessary to avoid centralization risks, and the initial stake distribution may lead to power imbalances. Proper design, testing, and maintenance are necessary due to increased complexity. Adoption should be based on network requirements and associated trade-off management.

Delegated byzantine proof of stake (DBPoS) : A secure and scalable system is achieved through a DPoS-PBFT hybrid algorithm. DPoS elects delegates for faster block confirmation and increased transaction throughput, while PBFT enhances resilience against failures. However, DPoS's small delegate set may lead to centralization and collusion risks, requiring proper governance and transparency. Careful design and monitoring are necessary to balance decentralization and efficiency. DPoS may affect decentralization compared to PBFT.

Choose machine-learning algorithms : Select ML algorithms, such as supervised, unsupervised, or rule-based learning, suitable for detecting and responding to attacks.

Steps involved in ML : ML approaches can be used to help prevent attacks on blockchain-based applications by:

Anomaly detection : ML algorithms can identify and flag unusual network behavior, allowing network participants to quickly detect and respond to potential attacks.

Prediction modeling : Predictive models can be trained to identify the likelihood of an attack based on previous data, allowing network participants to take preventative measures proactively.

Clustering and classification : Clustering and classification algorithms can be used to identify and categorize different attacks, making them easier to understand.

Network traffic analysis : Machine-learning algorithms can analyze network traffic and identify patterns that indicate potential attacks, allowing network participants to respond quickly.

Blockchain data analysis : Machine-learning algorithms can be used to analyze the data stored on the blockchain, such as transaction history and network activity, to identify potential attacks.

Fraud detection : ML algorithms can detect fraudulent transactions, such as double-spending or fake transactions, and prevent them from being added to the blockchain.

Risk assessment : ML algorithms can be used to assess the risk posed by different nodes on the network and prioritize security measures based on the risk level.

Reinforcement learning : ML algorithms can learn from network interactions and optimize the security measures to respond to potential attacks.

Data collection : Collect data from the required system to train the machine learning algorithms. This data should include normal and abnormal behavior patterns.

Train machine-learning models : Collect and label data from the system to train machine-learning algorithms, such as supervised, unsupervised, or rule-based learning, to detect and respond to cyber-attacks. This will help the algorithms identify abnormal behavior patterns that indicate an attack.

Integrate the consensus algorithm and machine learning models : Integrate the consensus algorithm and the machine learning models into the blockchain-based system such that the machine learning algorithms can identify and trigger a response mechanism through the consensus algorithm.

Implementing ML in the blockchain : Responsible machine learning (ML) involves developing, deploying, and using ML models that are ethical and accountable. This includes fairness, transparency, interpretability, and privacy. To achieve this, avoid biases and discrimination in data collection and model training, document decisions and assumptions, interpret predictions, and implement privacy safeguards. The ML life cycle includes formulation, acquisition, development, testing, deployment, and ongoing monitoring and maintenance. Responsible ML ensures the trustworthy and ethical use of ML technologies.

Performance metrics : To evaluate a machine-learning model, use metrics like Confusion Matrix, Accuracy, Precision, F1 Score, R-squared, ROC Curve, Area under ROC Curve, and Goodness of Fit. Analyzing these metrics helps identify areas for improvement and determine if the model suits the production environment.

Monitor the system : Continuously monitor the proposed system application and update the machine learning algorithms and consensus algorithm to adapt to evolving attack patterns.

Respond to an attack : If an attack is detected, the machine learning algorithms will trigger a response mechanism that is determined by the consensus algorithm. The response mechanism may include limiting the attacker's access, rolling back the blockchain, or triggering a secure emergency shutdown.

Test and evaluate : Test the hybrid system in a controlled environment to assess its effectiveness in detecting and responding to cyber-attacks.

Deploy : Once the system has been tested and evaluated, deploy the hybrid consensus algorithm and machine learning approach in the real-world environment.

Monitor and update : Continuously monitor the system for performance and security and update the ML and consensus algorithms to ensure their effectiveness against evolving attack patterns. However, implementing a hybrid consensus algorithm with an ML approach requires choosing an appropriate consensus algorithm, training ML models, integrating the consensus algorithm and ML models, testing and evaluating the system, and deploying the system.

Experimental results and discussion

Figure  4 shows the experimental diagram for this research work. In an IoT environment, obtaining vital information by deploying sensors that can detect various parameters from real-world scenarios is difficult. Establishing a robust system to collect this data in real-time or at regular intervals is paramount. Additionally, it is necessary to ensure the structured and balanced data and keep track of the time it was collected. Advanced techniques can be used to obtain valuable insights from the data. When training machine learning models, selecting and combining the most relevant features with labeled data is essential to building a training dataset.

Experimental test setup with intelligence analysis.

Furthermore, correct labeling of the target variable for machine learning is necessary. When setting up a Blockchain network, it is essential to choose a proposed consensus algorithm that fits the network's needs, such as Delegated Proof of Stake Work (DPoSW), Proof of Stake and Work (PoSW), Proof of CASBFT (PoCASBFT) Delegated Byzantine Proof of Stake (DBPoS). This algorithm determines how nodes agree on transactions and adds new blocks to the blockchain. Collecting data from the network is required to detect potential threats. Information like transactions, block details, timestamps, and network states help us stay keen and secure. Relevant features related to transaction volume, block size, transaction fees, or other parameters should be extracted to identify potential attacks. Feature extraction techniques can analyze transaction patterns, mining activities, block propagation delays, or network connectivity measures. Anomaly detection algorithms, such as statistical methods, machine learning techniques, or graph-based analysis, can be used to identify abnormal patterns or behaviors. If an anomaly is detected, it should be labeled as a specific type of attack, such as double-spending, selfish mining, Sybil attacks, or 51% attacks. Ensuring equal representation of typical and attack instances in the training dataset is crucial to boosting the model's performance. This approach is necessary to prevent bias and establish the proposed model to achieve the user's requirement.

After preparing the model, it can be implemented into an IoT environment to analyze future data or make predictions in real-time. In order to make decisions fast and accurately, it is necessary to integrate the model coherently with the current IoT infrastructure . Meanwhile, deploying the trained model in the blockchain network can help to detect and monitor potential attacks in real time. Integrating the model into the blockchain infrastructure enables proactive defense mechanisms against malicious activities. After preparing the model, it can be implemented into an IoT environment to analyze future data or make predictions in real-time. Integrating the model with the existing IoT infrastructure is necessitated to ensure efficient and effective decision-making. To ensure strong security measures, continuously monitor and improve the model by gathering new data and adapting to emerging attack patterns.

Improving consensus algorithms' performance depends on factors like block confirmation time, transaction throughput, energy efficiency, latency, scalability, and fault tolerance. In this work, we can enhance the algorithm's performance by fine-tuning its parameters using optimization algorithms, grid search, or random search techniques. They can then evaluate the optimal parameter values using metrics that reflect the desired performance factors. By comparing different parameter configurations, we can choose the best values that maximize the desired performance factors. By implementing this methodology, we anticipate a more promising and enhanced efficacy within blockchain technology. In blockchain networks, various changes occur, such as network size fluctuations, workload, latency, or adversarial activities. An adaptive consensus algorithm that can adjust its behavior as needed is required to achieve optimal performance. Our work involves developing rule-based mechanisms that dictate the circumstances in which the algorithm should adjust. These guidelines can be determined by various factors such as network parameters, performance metrics, or system-level thresholds. By adhering to these conditions, the algorithm can adapt to changing network conditions by modifying its mode or parameters. Machine learning or reinforcement learning techniques can be leveraged for intelligent adaptive selection. By training models on previous data and network conditions, algorithms can autonomously adapt based on learned patterns or reinforcement learning rewards and penalties. It is necessary to consider the preferences and needs of the users during the adaptive selection procedure. Consensus algorithms are crafted by integrating user-specified criteria that align with the unique requirements of the blockchain application, including but not limited to scalability, security, and energy efficiency.

Understanding the needs of stakeholders, identifying the priorities, and considering specific applications are essential for developing an adoptable ML-based blockchain network. Integrating Consensus Algorithms with ML approaches attains the potential to optimize the network performance and effectively respond to the user's requirements. Stakeholders may possess transaction throughput, privacy, decentralization, energy efficiency, or consensus speed-based priorities. Analyzing these requirements will introduce intelligent selection, which selects hybrid consensus algorithms such as Delegated Proof of Stake Work (DPoSW), Proof of Stake and Work (PoSW), Proof of CASBFT (PoCASBFT), and Delegated Byzantine Proof of Stake (DBPoS). The proposed algorithms have their strengths and trade-offs in security, scalability, efficiency, and decentralization. However, these performance factors are determined by finding the required algorithms based on the user's requirements.

Optimization of the consensus algorithm requires the adjustment of the network parameters based on the user requirements. This can be achieved through several optimization techniques. Here, an ML-based blockchain network has been developed to monitor the network's performance and allows the system to make an adaptive decision based on the feedback from the real-world scenario. Continuous monitoring, evaluation, model refinements with periodic assessments, experimentation based on stakeholders' responses, and user feedback are highly required to implement successful models. Valuable insights are gained by performing the investigation using ProximaX, and results are expected to overcome the different types of attacks in the proposed work. The expected results are listed below.

Real-time attack detection and response,

Maintenance of blockchain integrity,

The smooth operation of a proposed application,

Adaptability to evolving threats,

Improved security,

Minimized attack damage,

Efficient defense mechanism,

Increased trust in the system, adoption, and better performance.

The prime objective of this research work is to identify and respond to the attacks in real-time scenarios immediately. This can be achieved by implementing the ML model to determine the paranormal behavioral patterns that act as a threat. This improves the security and resilience of the blockchain network and minimizes the damage. Maintaining the integrity of the blockchain network is another important objective. Consensus algorithms can validate the transaction and ensure the reliability and stability of the Blockchain network. By combining these Consensus algorithms with a hybrid approach, the proposed system attains higher transactional data integrity and lowers unauthorized activities. ML model and Hybrid consensus mechanisms are combined to ensure convenient operations. These results in highly comprehensive defense mechanisms. This approach can reduce the downtime risk and provide effective operations. However, this approach is adaptable to an evolving new threat and maintains reliable and efficient operations in a specific application-oriented system. The proposed ML models can be kept updated and customized to encounter emerging attacks, which performs the guaranteed defense mechanism for real-world environments.

This approach can proactively identify, detect, respond to, and mitigate the threats. However, it can also reduce the system vulnerability and damages caused by the threats. Expeditious measures are performed to protect the stability and functionality of the network, ensuring better system operations. The proposed hybrid approaches can efficiently protect the blockchain-based system against attacks. The proposed method adapts and updates continuously, which performs fine-tuning to understand emerging threats. This also enhances trust and encourages adaptability in intelligent grid sectors. These features can increase the stakeholder's confidence and improve system performance, resulting in stability, security, and efficient defense against attacks.

Security enhancements achieved through the proposed solution

By combining ML techniques with consensus protocols, the blockchain network performs anomaly detection, adaptive decision-making, and detection of malicious activities. These techniques can detect and mitigate various cyber-attacks by continuous network monitoring and analyzing real-time datasets. However, this hybrid approach provides robust security enhancements that enhance decision-making. This section discusses the various security enhancements achieved through the proposed methodology.

Increased attack resistance : Incorporating ML techniques and hybrid consensus protocols in blockchain networks increases the attack's resistance. This identifies and prevents attacks such as 51%of attacks, Sybil, and double spending attacks. ML models can detect potential attacks by collecting transaction details, network connectivity, and participant behavior. However, the proposed method ensures network security and integrity. This ML model analyzes the network behavior and transaction patterns using previous data obtained from the network, which helps to detect malicious activities and perform precautionary measures. This can prevent almost half of the attacks, detect malicious mining behaviors, and immediately send alert information to prevent attacks. These proactive techniques will minimize the negative impacts of the blockchain network and maintain high security, which is appreciable for the stakeholders and network participants.

Furthermore, the ML model can prevent Sybil and double spending attacks on blockchain networks. These models can use the previous network information and detect irregularities, flags, and fraudulent activities. Combining ML algorithms with a hybrid consensus process will improve the network's ability, promoting confidence in the blockchain network and protecting the stakeholder's assets and transaction details.

Dynamic threat detection : ML algorithms play a crucial role in detecting new threats and identifying the attacking patterns in the blockchain network. The proposed ML model can identify the unusual behaviors and patterns that represent potential attacks by analyzing the datasets from the existing networks. In addition, continuous learning in ML techniques leads to the immediate adaption of new approaches used by malicious attackers, which develop the network into highly secure. These algorithms can detect attacks, including DDoS, Network intrusions, data breaches, and malware propagation. ML model can perform dynamic threat detection, which enhances blockchain security and promotes trust among the network participants and stakeholders.

Anomaly detection and prevention : Effective anomaly detection and introducing preventive measures lead to maintaining the high security and integrity of blockchain networks. ML models are irreplaceable tools that detect mischievous transaction behavior, network connectivity, and other participant activities. It can perform proactive detection and prevent suspicious activities. Analyzing transaction records, network logs, participant interactions, and other system parameters leads the ML model to learn the standard patterns and behaviors exhibited within the network. However, these models establish the expected baseline and detect the deviations from these patterns that indicate fraudulent activities. ML techniques such as clustering, outlier detection, time-series analysis, and graph-based techniques are used to identify potential attacks. Integrating these techniques with blockchain networks can enhance proactive identification, which detects malicious activities in real time.

Furthermore, anomaly detection can deter potential attackers and discourage fraudulent activities . This can improve the confidence level of the stakeholders and participants and promote the adoption of blockchain technology. The proposed ML model can integrate the network decision-making process. For instance, detecting anomalies leads the network to trigger additional verification automatically and employs priority-based security over efficiency. This adaptive response makes the system resilient, secure, and trustworthy to face potential threats.

Adaptive decision-making : ML models facilitate the blockchain networks based on previous data in real time and adapt the decision-making process. This systematic approach can improve performance and optimize security by investigating network congestion and resource availability. We can analyze the metrics and adjust the consensus parameters model with ML approaches. This leads to block time and size variations that improve the transaction throughput and network performance in congested networks. Similarly, for detecting malicious behavior, the proposed model suggests actions such as improving validation regulations and tweaking the consensus protocols. These models can be trained based on previous data, which performs the adaptive choices to enhance security. It can identify patterns or trends that affect network performance and security by analyzing experiences. This knowledge allows the models to make informed decisions and adjust the consensus parameters accordingly. Adaptive decision-making is essential for blockchain networks because it enables quick response to changes by adjusting real-time consensus parameters. It also enhances security by detecting and responding to emerging threats using ML models. This approach optimizes resource allocation, improving efficiency, scalability, and network participation. Continuously adapting decision-making strategies ensures the network remains responsive to emerging challenges and continually improves performance, security, and resilience.

Robust network monitoring : The safety and stability of blockchain networks heavily rely on dependable monitoring. Hybrid consensus protocols use ML to continuously monitor and analyze the network, detecting security threats, attacks, and irregular behaviors. ML algorithms are particularly effective at identifying patterns and anomalies in large amounts of data, such as traffic and transaction data. Monitoring the blockchain network in real-world scenarios provides earlier warnings and other appropriate trigger responses. Robust monitoring can detect critical behavior, potential attacks such as DDoS, Sybil, tampering with the data transactions records, and other security breaches. This also uncovers the security vulnerability and pattern with exploitable loopholes, mitigates the attacks, and maintains the network integrity. ML models address these issues proactively and improve the system's ability to fight security threats and enhance overall network security. Furthermore, these models can identify recurring patterns and adjust their response strategies to improve outcomes in strengthening high adaptability. By using federated learning and homomorphic encryption, consensus mechanisms can detect anomalies and security breaches, improving the privacy and security of the entire blockchain network.

Scalability, decentralization, and energy efficiency considerations

The hybridization of consensus algorithms and ML models addresses challenges such as scalability, decentralization, and energy efficiency in distributed systems. This approach can disseminate the workload across various machines, perform effective data processing, and execute parallel algorithms. The decentralization process can be achieved using P2P networks, which improves reliability and prevents failures. To ensure data availability and integrity, redundancy and replication techniques like sharding or erasure coding can be used, even in node failures. Energy efficiency has become a critical concern as the demand for sustainable computing solutions grows. Consensus algorithms and ML models should be hybridized while considering energy efficiency to minimize environmental impact and operating costs. Use specialized hardware or cloud services to save energy when performing resource-intensive tasks like machine learning or complex consensus computations. These specialized infrastructures are designed to maximize computational efficiency, reducing energy requirements. Developing consensus algorithms with energy efficiency in mind can also minimize computational and communication overhead. PoS consumes less energy than PoW algorithms, relying on stakeholder voting rather than resource-intensive mining. ML models can be compressed or quantized using techniques like pruning, quantization, and knowledge distillation to reduce their size and energy consumption without sacrificing accuracy. Hybridizing consensus algorithms and machine learning models can address distributed systems' scalability, decentralization, and energy efficiency challenges. Considering these factors, we can develop sustainable solutions that leverage the best of both worlds.

Advantages and optimizations achieved through the proposed approach

Hybrid consensus algorithms and ML approaches are widely used to overcome various attacks in blockchain technology. The advantages and optimizations achieved through the proposed approaches are listed below. The key insights and SWOT analysis of the proposed research work are shown in Fig.  5

SWOT analysis of the hybrid approach.

Improved security

Advanced security is necessary for safe and reliable blockchain network systems. Practical ML techniques with hybrid consensus create a secured network with high integrity and reliability, preventing potential attacks and negative impacts. This model can establish a trust-based framework that ensures the network nodes perform valid transactions and avoid unauthorized attackers. Furthermore, some attacks require other measures that represent abnormal behavior. ML techniques can investigate substantial data volumes, identify patterns, and detect malicious attacks in real time. Here, continuous monitoring of energy transactions, communication patterns, and other system parameters is performed by integrating ML techniques. The system can utilize the previous data and effectively identify the unusual activity of ongoing attacks. Combining consensus and ML algorithms can create a robust defense mechanism and prevent cyber-attacks. Integrating ML techniques leads the system to detect and prevent the attack before any damage. These techniques can adapt and improvise through continuous learning from new data and evolving attack techniques. This adaptability can improve the system's ability to detect threats and respond effectively to unknown attacks, strengthening security.

Real-time detection and response

Real-time detection over frequent responses is necessary to ensure the security and stability of the proposed systems. ML techniques are generally trained on the previous data and establish the baseline to understand normal behavior, energy consumption, communication patterns, system metrics, and other relevant data. By identifying the anomalies, ML techniques can provide the response mechanisms to isolate the affected nodes, reroute the energy transactions, and activate the alerts and backup systems for further investigation. For example, The proposed system with microgrid application can provide services to hospitals, emergency services, and remote community sectors, requiring real-time detection and responses and ensuring uninterrupted operations and reliability. ML techniques can identify the attacks in time, respond quickly to process the data and ensure the decision is based on learned patterns. This system can ensure security, detect real-time threats, and reduce the vulnerability window that causes minimum damage by utilizing ML techniques.

Evolving security

Evolving threats can be avoided using hybrid consensus and ML algorithms. Consensus algorithms can secure the blockchain, and ML techniques analyze various data sources and detect new attacks, adapting defense systems to ensure security. Data analysis tools can monitor parameters such as network traffic, transaction patterns, user behavior, and other system logs. These systems can analyze new data, improve their ability to detect suspicious activities and perform immediate alerts for further investigation. ML algorithms can perform collaborative acts and rely on information sharing for evolving defense mechanisms. Integrating consensus and ML algorithms can strengthen the synergistic effect and be updated regularly through ongoing research, collaboration, and information sharing within the blockchain community. This system can stay connected to the latest attack techniques, which helps to mitigate emerging threats and ensures long-term security.

Efficient defense mechanism

An effective defense system can be developed using consensus and ML algorithms that detect and respond automatically. In microgrid applications, consensus algorithms can provide authorized transactions that ensure integrity and reliability. ML algorithms can analyze the data, identify patterns, learn from previous attacks, and predict the detection. These ML models will monitor the microgrid system, identify network behavior, and analyze the transaction patterns. This leads to the proposed algorithm detecting potential attacks, identifying the deviations, evolving the triggering process, and mitigating their impacts on microgrid security. Combining hybrid consensus algorithms and ML techniques creates an effective defense mechanism that performs an immediate detection and response to any attacks on the microgrid. Through continuous learning, the ML model develops into an automation process that reduces the administrator's workload, enhances the efficiency of the defense mechanism, and enables real-time detection and response to attacks. These processes can be optimized through iterative learning approaches in ML techniques. Through the application of these methodologies, the proposed system has the potential to enhance the resilience and efficiency of microgrid systems . These algorithms can automate processes and continuously learn, which reduces the workload on system administrators and promotes a proactive approach to security. Ultimately, this helps to defend against attacks while ensuring the uninterrupted operation of the system.

Increased trust in the system

Trust and security are crucial in microgrid systems, and blockchain-based microgrid systems offer a solution that goes beyond. Combining hybrid consensus algorithms and ML approaches can enhance trust in the system's design and improve its adaptability. Using hybrid consensus algorithms and machine learning techniques creates a robust defense system to protect microgrids against potential attacks. This way, it establishes trust by verifying transaction validity and preventing malicious activities through consensus among network participants while also allowing for continuous learning from new data and emerging attack patterns through ML algorithms. Adaptability is of utmost importance in maintaining the effectiveness of defense mechanisms in microgrid systems, especially against constantly evolving and sophisticated strategies. Through ML algorithms, this hybridization can detect anomalies, predict real-time attack patterns, and adjust the defense mechanism accordingly. As a result, this instills increased trust in the microgrid system, enhances its resilience, and reduces its vulnerability to evolving threats.

Open issues and challenges of the hybrid consensus approach

Open issues of the proposed research approach.

Integrating ML, deep learning, and RL with blockchain protocols can improve security, performance, and decision-making capabilities. However, it also presents open issues and challenges that researchers and practitioners must consider carefully. In order to discover the benefits of these technologies in blockchain networks, it is essential to understand and address these challenges. This research analyzes the challenges of integrating intelligent learning algorithms with consensus protocols in blockchain networks. However, it also explores the factors researchers and practitioners should consider to ensure blockchain networks' effective and efficient operation. This analysis identifies critical limitations such as machine-learning algorithms' computational complexity and resource requirements, the need for labeled training data in decentralized and pseudonymous blockchain networks, and adaptive learning approaches in dynamic blockchain networks. Investigations are performed to understand the significance of security and privacy in integrating ML techniques, which includes the vulnerability of models, malicious attacks, and other challenges that ensure interpretability in decision-making processes. However, it is observed that valuable insights into the difficulties of Implementing ML-based consensus algorithms in blockchain. Our findings can also provide future research and development efforts to address the practical concerns and overcome these limitations.

Incorporating hybrid consensus algorithms with ML models will be challenging and complex. To achieve this, we need a strong understanding of consensus protocol, ML algorithms, and sufficient computational resources for training and executing the models. Coordinating the computational resources with consensus protocol and integrating the ML approaches into the hybridization are significant challenges in real-world scenarios.

Data availability and quality

Accurate training of ML models requires diverse and high-quality data. Nevertheless, this can be challenging in blockchain networks, where data privacy and confidentiality are crucial. Furthermore, obtaining labeled data for supervised learning requires specialized expertise and extensive manual work that can be time-consuming and expensive.

Model robustness and generalization

For developing successful hybrid consensus algorithms, the ML model must possess resilience and the capability to perform effective generalization in untested data. Inadequate adaptation to new attack patterns or overfitting can compromise the effectiveness of the models, leading to the identification of false positives or false negatives, which can ultimately weaken the entire system.

Interpretability and explainability

Understanding the reasoning behind ML models can be difficult, as they often function like mysterious black boxes. However, when it comes to consensus protocols, transparency and accountability are crucial, which means that methods must be employed to clarify the choices made by these models. This task can be incredibly daunting when dealing with complex models such as deep learning architectures.

Malicious attacks

It is challenging to be aware of potential threats to the machine learning models, such as false training data, harmful adversarial examples, and weaknesses in the learning process. These attacks can negatively affect the reliability and effectiveness of the hybrid consensus approach. Therefore, it is essential to implement robust defenses to minimize their impact.

Computational overhead

Incorporating ML models into the consensus process could increase the amount of computational work needed. Developing and implementing intricate machine learning models may demand substantial computational resources, which could affect the scalability and effectiveness of the blockchain network.

Ethical considerations

It is imperative to consider ethical implications such as privacy, fairness, and bias when utilizing ML models in consensus protocols. We must take precautions to prevent infringement on user privacy or discriminatory behavior towards certain participants due to biased training data or decision-making processes. Addressing these issues will lead to more effective and equitable use of ML models.

Regulatory and legal considerations

Applying ML models in consensus protocols may raise regulatory and legal challenges, especially in industries with strict data protection and compliance requirements. Compliance with data privacy regulations and ensuring the lawful use of data becomes crucial when integrating ML into consensus mechanisms. Addressing these limitations and challenges requires careful consideration of the specific use case, continuous research and development, and collaboration between domain experts in consensus protocols, machine learning, and cybersecurity. By addressing these challenges, the hybrid consensus approach with ML models can unlock its full potential in enhancing blockchain networks' security, scalability, and adaptability.

Future research directions

The future scope of this research leads to adaptive hybrid models, which focus on developing new consensus mechanisms and ML techniques. These combinations can dynamically adapt to networking situations. It also includes switching between other ML models or consensus mechanisms and responding to the security threats that evolve based on the scenario.

Privacy-preserving ML techniques are essential for advancing research. These hybrid models effectively integrate homomorphic encryption, federated learning, and differential privacy. This ensures that the user's sensitive information remains secure and confidential while performing the learning process.

Future research should focus on self-learning systems, which have the potential to hybrid consensus mechanisms. These systems can adapt autonomously to new threats and optimize the system parameters per the network conditions' real-time feedback. However, energy efficiency is the paramount factor in the blockchain network. Research efforts should focus on developing effective energy consumption-based consensus mechanisms and ML models.

The security and the consensus mechanism's performance can be ensured by establishing security standards and benchmarks for the evaluation process. This facilitates comparing various approaches and contributes to developing new effective models. Cross-collaboration across experts in consensus algorithms, machine learning, cryptography, and cyber security leads to novel methods and insights to overcome real-world problems.

In this research, we have thoroughly analyzed the hybrid consensus algorithm with ML techniques. The challenges and vulnerabilities that exist in a proposed system are concluded using a ProximaX-based decentralized network platform. The findings from this research paper have emphasized the need for effective preventive measures to combat the detrimental impact of cyber-attacks. The proposed research uses hybridized consensus approaches to enhance network security, scalability, and resilience. The proposed ML-based hybrid consensus algorithm mitigates the challenges and vulnerabilities in decentralized public networks. The ML model implemented in this research elevates threat detection, optimizes consensus mechanisms, and ensures the confidentiality of transactions and user data. The decentralized nature of ML-driven security performs proactive attack identification, feature extraction, and anomaly detection, enhancing the consensus protocol's security and reducing cyber-attacks. The proposed model understands the consensus mechanism and retrieves the real-time data and network state, which improves network resilience and decision-making accuracy in the dynamic field of cyber security.

Furthermore, this paper also discusses the implementation challenges of the proposed consensus approaches and their adaptability to a real-world scenario. Further investigation and refinements are required to solve the complexity of the ML model with scalability, resource requirements, computational overhead, and susceptibility to achieve the effectiveness of security and trustworthiness. The future scope of this research leads to the development of an adaptive hybrid model that utilizes novel consensus mechanisms and ML techniques. Privacy-preserving ML techniques and generative learning can autonomously adapt to new threats and optimize the system parameters based on the real-world environment.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Oyinloye, D. P., Sen Teh, J., Jamil, N. & Alawida, M. Blockchain consensus: An overview of alternative protocols. Symmetry 13 (8), 1363 (2021).

Article   ADS   Google Scholar  

Wang, B., Li, Z. & Li, H. Hybrid consensus algorithm based on modified proof-of-probability and DPoS. Futur. Internet 12 (8), 1–16 (2020).

Article   Google Scholar  

Shafay, M. et al. Blockchain for deep learning: Review and open challenges. Cluster Comput. 26 (1), 197–221 (2023).

Article   PubMed   Google Scholar  

Khobragade, P. & Turuk, A. K. Blockchain consensus algorithms: A survey. Lect. Notes Netw. Syst. 595 , 198–210 (2023).

Bachani, V. & Bhattacharjya, A. Preferential delegated proof of stake (PDPoS)—modified DPoS with two layers towards scalability and higher TPS. Symmetry 15 (1), 4 (2023).

Wu, Y., Song, P. & Wang, F. Hybrid consensus algorithm optimization: A mathematical method based on POS and PBFT and its application in blockchain. Math. Probl. Eng. https://doi.org/10.1155/2020/7270624 (2020).

Sakhnini, J., Karimipour, H. & Dehghantanha, A. smart grid cyber attacks detection using supervised learning and heuristic feature selection. in Proceeding of 2019 7th International Conference on Smart Energy Grid Engineering SEGE 2019 , pp. 108–112, (2019).

Mololoth, V. K., Saguna, S. & Åhlund, C. Blockchain and machine learning for future smart grids: A review. Energies 16 (1), 528 (2023).

Ortega-fernandez, I. & Liberati, F. Smart grid using reinforcement learning. 1–15 (2023).

Sahani, N., Zhu, R., Cho, J. H. & Liu, C. C. Machine learning-based intrusion detection for smart grid computing: A survey. ACM Trans. Cyber-Phys. Syst. 7 (2), 1–31 (2023).

Kiayias, A., Russell, A., David, B. & Oliynykov, R. Ouroboros: A provably secure proof-of-stake blockchain protocol , LNCS. 10401, (2017).

Lashkari, B. & Musilek, P. A comprehensive review of blockchain consensus mechanisms. IEEE Access 9 , 43620–43652 (2021).

Xiao, Y., Zhang, N., Lou, W. & Hou, Y. T. A survey of distributed consensus protocols for blockchain networks. IEEE Commun. Surv. Tutorials 22 (2), 1432–1465 (2020).

Andoni, M. et al. Blockchain technology in the energy sector: A systematic review of challenges and opportunities. Renew. Sustain. Energy Rev. 100 , 143–174 (2019).

Wang, W. et al. A survey on consensus mechanisms and mining strategy management in blockchain networks. IEEE Access 7 , 22328–22370 (2019).

Yang, F. et al. Delegated proof of stake with downgrade: A secure and efficient blockchain consensus algorithm with downgrade mechanism. IEEE Access 7 , 118541–118555 (2019).

Shah, A. A., Malik, H. A. M., Muhammad, A. H., Alourani, A. & Butt, Z. A. Deep learning ensemble 2D CNN approach towards the detection of lung cancer. Sci. Rep. 13 (1), 1–15 (2023).

Wang, X. et al. A long single-span dispersion-decreasing-like fiber transmission system. Opt. Laser Technol. 116 , 338–344 (2019).

Razali, N. F., Isa, I. S., Sulaiman, S. N., Noor, N. K. & Osman, M. K. CNN-Wavelet scattering textural feature fusion for classifying breast tissue in mammograms. Biomed. Signal Process. Control 83 , 104683 (2023).

Yazdinejad, A., Parizi, R. M., Dehghantanha, A. & Choo, K. K. R. P4-to-blockchain: A secure blockchain-enabled packet parser for software defined networking. Comput. Secur. 88 , 101629 (2020).

Alam, T., Ullah, A. & Benaida, M. Deep reinforcement learning approach for computation offloading in blockchain-enabled communications systems. J. Ambient Intell. Humaniz. Comput. 14 (8), 9959–9972 (2023).

Sanwar Hosen, A. S. M. et al. Blockchain-based transaction validation protocol for a secure distributed IoT network. IEEE Access 8 , 117266–117277 (2020).

Michalski, R., Dziubaltowska, D. & MacEk, P. Revealing the character of nodes in a blockchain with supervised learning. IEEE Access 8 , 109639–109647 (2020).

Nasir, M. U., Khan, S., Mehmood, S., Khan, M. A., Zubair, M. & Hwang, S. O. Empowered with blockchain technology. (2022).

Zeadally, S. & Tsikerdekis, M. Securing internet of things (IoT) with machine learning. Int. J. Commun. Syst. 33 (1), 1–16 (2020).

Joshi, K. et al. Machine-learning techniques for predicting phishing attacks in blockchain networks: A comparative study. Algorithms 16 (8), 366 (2023).

Khan, I. A. et al. Fed-inforce-fusion: A federated reinforcement-based fusion model for security and privacy protection of IoMT networks against cyber-attacks. Inf. Fusion 101 , 102002 (2023).

Khan, I. A. et al. A privacy-conserving framework based intrusion detection method for detecting and recognizing malicious behaviours in cyber-physical power networks. Appl. Intell. 51 (10), 7306–7321 (2021).

Khan, I. A. et al. Enhancing IIoT networks protection: A robust security model for attack detection in internet industrial control systems. Ad Hoc Netw. 134 , 102930 (2022).

Khan, I. A. et al. An enhanced multi-stage deep learning framework for detecting malicious activities from autonomous vehicles. IEEE Trans. Intell. Transp. Syst. 23 (12), 25469–25478 (2022).

Sapra, N., Shaikh, I. & Dash, A. Impact of proof of work (PoW)-based blockchain applications on the environment: A systematic review and research agenda. J. Risk Financ. Manag. 16 (4), 218 (2023).

Schinckus, C. Proof-of-work based blockchain technology and anthropocene: An undermined situation?. Renew. Sustain. Energy Rev. 152 , 111682 (2021).

Saad, M., Njilla, L., Kamhoua, C., Kim, J., Nyang, D. & Mohaisen, A. Mempool optimization for defending against DDoS attacks in PoW-based blockchain systems. in ICBC 2019–IEEE International Conference on Blockchain and Cryptocurrency , pp. 285–292 (2019).

Li, W., Cao, M., Wang, Y., Tang, C. & Lin, F. Mining pool game model and nash equilibrium analysis for PoW-based blockchain networks. IEEE Access 8 , 101049–101060 (2020).

Lepore, C. et al. A survey on blockchain consensus with a performance comparison of PoW, PoS and pure PoS. Mathematics 8 (10), 1–26 (2020).

Cao, B. et al. Performance analysis and comparison of PoW, PoS and DAG based blockchains. Digit. Commun. Networks 6 (4), 480–485 (2020).

Liu, D., Alahmadi, A., Ni, J., Lin, X. & Shen, X. Anonymous reputation system for IIoT-enabled retail marketing atop PoS blockchain. IEEE Trans. Ind. Inform. 15 (6), 3527–3537 (2019).

Xu, G., Liu, Y. & Khan, P. W. Improvement of the DPoS consensus mechanism in blockchain based on vague sets. IEEE Trans. Ind. Inform. 16 (6), 4252–4259 (2020).

Zhou, T., Li, X. & Zhao, H. DLattice: A permission-less blockchain based on DPoS-BA-DAG consensus for data tokenization. IEEE Access 7 , 39273–39287 (2019).

Liu, J., Xie, M., Chen, S., Ma, C. & Gong, Q. An improved DPoS consensus mechanism in blockchain based on PLTS for the smart autonomous multi-robot system. Inf. Sci. 575 , 528–541 (2021).

Article   MathSciNet   Google Scholar  

Chen, S., Xie, M., Liu, J. & Zhang, Y. Improvement of the DPoS consensus mechanism in blockchain based on PLTS. in Proceeding of–2021 7th IEEE Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing,(HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS) , pp. 32–37 (2021).

Wang, L., Xu, P., Su, W., Li, Y. & Chen, X. Research on Improvement of blockchain DPOS consensus mechanism based on HK clustering. in Proceeding–2021 China Autom. Congr. CAC 2021 , pp. 1167–1172 (2021).

Li, W. et al. A scalable multi-layer PBFT consensus for blockchain. IEEE Trans. Parallel Distrib. Syst. 32 (5), 1146–1160 (2021).

Chiu, W. Y. & Meng, W. EdgeTC–a PBFT blockchain-based ETC scheme for smart cities. Peer-to-Peer Netw. Appl. 14 (5), 2874–2886 (2021).

Liu, J., Feng, W., Zhang, Y. & He, F. Improvement of PBFT algorithm based on CART. Electronics 12 (6), 1460 (2023).

Liu, S., Zhang, R., Liu, C. & Shi, D. P-PBFT: An improved blockchain algorithm to support large-scale pharmaceutical traceability. Comput. Biol. Med. 154 , 106590 (2023).

Wu, Y., Wu, L. & Cai, H. Reinforced practical byzantine fault tolerance consensus protocol for cyber physical systems. Comput. Commun. 203 , 238–247 (2023).

Hu, Y. et al. A practical heartbeat-based defense scheme against cloning Attacks in PoA blockchain. Comput. Stand. Interfaces 83 , 103656 (2023).

Ometov, A. et al. An overview on blockchain for smartphones: state-of-the-art, consensus, implementation, challenges and future trends. IEEE Access 8 , 103994–104015 (2020).

Sasikumar, A. et al. Blockchain-based trust mechanism for digital twin empowered industrial internet of things. Futur. Gener. Comput. Syst. 141 , 16–27 (2023).

Shi, L., Wang, T., Li, J., Zhang, S. & Guo, S. Pooling is not favorable: Decentralize mining power of PoW blockchain using age-of-work. IEEE Trans. Cloud Comput. 11 (3), 2756–2769 (2022).

Google Scholar  

Bandara, E. et al. Casper: A blockchain-based system for efficient and secure customer credential verification. J. Bank. Financ. Technol. 6 (1), 43–62 (2022).

Zhu, S., Cai, Z., Hu, H., Li, Y. & Li, W. zkCrowd: A hybrid blockchain-based crowdsourcing platform. IEEE Trans. Ind. Inform. 16 (6), 4196–4205 (2020).

Buterin, V., Reijsbergen, D., Leonardos, S. & Piliouras, G. Incentives in Ethereum’s hybrid Casper protocol. in ICBC 2019–IEEE International Conference of Blockchain Cryptocurrency , pp. 236–244 (2019).

Sriman, B., Ganesh Kumar, S. & Shamili, P. Blockchain technology: consensus protocol proof of work and proof of stake. Adv. Intell. Syst. Comput. 1172 , 395–406 (2021).

Chen, Y. & Liu, F. Research on improvement of DPoS consensus mechanism in collaborative governance of network public opinion. Peer-to-Peer Netw. Appl. 15 (4), 1849–1861 (2022).

Hasanova, H., Baek, U. J., Shin, M. G., Cho, K. & Kim, M. S. A survey on blockchain cybersecurity vulnerabilities and possible countermeasures. Int. J. Netw. Manag. 29 (2), 1–36 (2019).

Schlatt, V., Guggenberger, T., Schmid, J. & Urbach, N. Attacking the trust machine: Developing an information systems research agenda for blockchain cybersecurity. Int. J. Inf. Manag. 68 , 102470 (2023).

Chaganti, R., Bhushan, B. & Ravi, V. A survey on blockchain solutions in DDoS attacks mitigation: Techniques, open challenges and future directions. Comput. Commun. 197 , 96–112 (2023).

Bhardwaj, A. et al. Penetration testing framework for smart contract Blockchain. Peer-to-Peer Netw. Appl. 14 (5), 2635–2650 (2021).

Liao, K. & Katz, J. Incentivizing Blockchain Forks via Whale Transactions. In Financial cryptography and data security. FC 2017. Lecture notes in computer science Vol. 10323 (eds Brenner, M. et al. ) 264–279 (Springer, 2017).

Iqbal, M. & Matulevicius, R. Exploring sybil and double-spending risks in blockchain systems. IEEE Access 9 , 76153–76177 (2021).

Xu, C. et al. A lightweight and attack-proof bidirectional blockchain paradigm for internet of things. IEEE Internet Things J. 9 (6), 4371–4384 (2022).

Nicolas, K., Wang, Y., Giakos, G. C., Wei, B. & Shen, H. Blockchain system defensive overview for double-spend and selfish mining attacks: A systematic approach. IEEE Access 9 , 3838–3857 (2021).

Li, B. et al. LBS meets blockchain: An efficient method with security preserving trust in SAGIN. IEEE Internet Things J. 9 (8), 5932–5942 (2022).

Nofer, M., Gomber, P., Hinz, O. & Schiereck, D. Blockchain. Bus. Inf. Syst. Eng. 59 (3), 183–187 (2017).

Shin, D. D. H. Blockchain: The emerging technology of digital trust. Telemat. Inform. 45 , 101278 (2019).

Kumar, R. & Sharma, R. Leveraging blockchain for ensuring trust in IoT: A survey. J. King Saud Univ.-Comput. Inf. Sci. 34 (10), 8599–8622 (2022).

Anjum, A., Sporny, M. & Sill, A. Blockchain standards for compliance and trust. IEEE Cloud Comput. 4 (4), 84–90 (2017).

Ismail, L. & Materwala, H. A review of blockchain architecture and consensus protocols: Use cases, challenges, and solutions. Symmetry 11 (10), 1198 (2019).

Khatri, N., Shrestha, R. & Nam, S. Y. Security issues with in-vehicle networks, and enhanced countermeasures based on blockchain. Electronics 10 (8), 1–33 (2021).

Pourrahmani, H., Yavarinasab, A., Monazzah, A. M. H. & Van Herle, J. A review of the security vulnerabilities and countermeasures in the Internet of Things solutions: A bright future for the blockchain. Internet Things 23 , 100888 (2023).

Xia, J., Li, H. & He, Z. The effect of blockchain technology on supply chain collaboration: A case study of Lenovo. Systems 11 (6), 299 (2023).

Connolly, L. Y. & Wall, D. S. The rise of crypto-ransomware in a changing cybercrime landscape: Taxonomising countermeasures. Comput. Secur. 87 , 101568 (2019).

Nguyen, C. T. et al. Proof-of-stake consensus mechanisms for future blockchain networks: Fundamentals, applications and opportunities. IEEE Access 7 , 85727–85745 (2019).

Yusoff, J., Mohamad, Z. & Anuar, M. A review: Consensus algorithms on blockchain. J. Comput. Commun. 10 (09), 37–50 (2022).

Fu, X., Wang, H. & Shi, P. A survey of blockchain consensus algorithms: mechanism, design and applications. Sci. China Inf. Sci. 64 (2), 1–15 (2021).

Bamakan, S. M. H., Motavali, A. & Babaei Bondarti, A. A survey of blockchain consensus algorithms performance evaluation criteria. Expert Syst. Appl. 154 , 113385 (2020).

Foti, M., Mavromatis, C. & Vavalis, M. Decentralized blockchain-based consensus for optimal power flow solutions. Appl. Energy 283 , 116100 (2021).

Shahsavari, Y., Zhang, K. & Talhi, C. Toward quantifying decentralization of blockchain networks with relay nodes. Front. Blockchain 5 , 1–11 (2022).

Yadav, A. K. et al. A comparative study on consensus mechanism with security threats and future scopes: Blockchain. Comput. Commun. 201 , 102–115 (2023).

Liu, Y., Ke, J., Xu, Q., Jiang, H. & Wang, H. Decentralization is vulnerable under the gap game. IEEE Access 7 , 90999–91008 (2019).

Kim, H., Kim, S. H., Hwang, J. Y. & Seo, C. Efficient privacy-preserving machine learning for blockchain network. IEEE Access 7 , 136481–136495 (2019).

Farooq, M. S. et al. Blockchain-based smart home networks security empowered with fused machine learning. Sensors 22 (12), 1–13 (2022).

Miglani, A. & Kumar, N. Blockchain management and machine learning adaptation for IoT environment in 5G and beyond networks: A systematic review. Comput. Commun. 178 , 37–63 (2021).

Ali, J., Khan, R., Ahmad, N. & Maqsood, I. Random forests and decision trees. IJCSI Int. J. Comput. Sci. Issues 9 (5), 272–278 (2012).

Ren, Y. S., Ma, C. Q., Kong, X. L., Baltas, K. & Zureigat, Q. Past, present, and future of the application of machine learning in cryptocurrency research. Res. Int. Bus. Financ. 63 , 101799 (2022).

Ferrag, M. A. & Maglaras, L. DeepCoin: A novel deep learning and blockchain-based energy exchange framework for smart grids. IEEE Trans. Eng. Manag. 67 (4), 1285–1297 (2020).

Amirzadeh, R., Nazari, A. & Thiruvady, D. Applying artificial intelligence in cryptocurrency markets: A survey. Algorithms 15 (11), 428 (2022).

Keshk, M., Turnbull, B., Moustafa, N., Vatsalan, D. & Choo, K. K. R. A privacy-preserving-framework-based blockchain and deep learning for protecting smart power networks. IEEE Trans. Ind. Inform. 16 (8), 5110–5118 (2020).

Jameel, F. et al. Reinforcement learning in blockchain-enabled IIoT networks: A survey of recent advances and open challenges. Sustainability 12 (12), 1–22 (2020).

Zhang, F., Wang, H., Zhou, L., Xu, D. & Liu, L. A blockchain-based security and trust mechanism for AI-enabled IIoT systems. Futur. Gener. Comput. Syst. 146 , 78–85 (2023).

Dai, Y. et al. Blockchain and deep reinforcement learning empowered intelligent 5G beyond. IEEE Netw. 33 (3), 10–17 (2019).

Qiu, C., Ren, X., Cao, Y. & Mai, T. Deep reinforcement learning empowered adaptivity for future blockchain networks. IEEE Open J. Comput. Soc. 2 , 99–105 (2020).

Mohammed, Z. K. et al. Bitcoin network-based anonymity and privacy model for metaverse implementation in Industry 5.0 using linear Diophantine fuzzy sets. Ann. Oper. Res. https://doi.org/10.1007/s10479-023-05421-3 (2023).

Jang, H. & Lee, J. An empirical study on modeling and prediction of bitcoin prices with bayesian neural networks based on blockchain information. IEEE Access 6 , 5427–5437 (2017).

Raja, L. & Periasamy, P. S. A trusted distributed routing scheme for wireless sensor networks using block chain and jelly fish search optimizer based deep generative adversarial neural network (deep-GANN) technique. Wirel. Pers. Commun. 126 (2), 1101–1128 (2022).

Elsayed, R., Hamada, R., Hammoudeh, M., Abdalla, M. & Elsaid, S. A. A Hierarchical deep learning-based intrusion detection architecture for clustered internet of things. J. Sens. Actuator Netw. 12 (1), 3 (2023).

Dwivedi, A. D., Srivastava, G., Dhar, S. & Singh, R. A decentralized privacy-preserving healthcare blockchain for IoT. Sensors 19 (2), 1–17 (2019).

Viswanadham, Y. V. R. S. & Jayavel, K. A framework for data privacy preserving in supply chain management using hybrid meta-heuristic algorithm with ethereum blockchain technology. Electronics 12 (6), 1404 (2023).

Ogundokun, R. O., Misra, S., Maskeliunas, R. & Damasevicius, R. A review on federated learning and machine learning approaches: categorization, application areas, and blockchain technology. Information 13 (5), 263 (2022).

Mekdad, Y. et al. A survey on security and privacy issues of UAVs. Comput. Networks 224 , 362–367 (2023).

Frimpong, S. A. et al. RecGuard: An efficient privacy preservation blockchain-based system for online social network users. Blockchain Res. Appl. 4 (1), 100111 (2023).

Qi, J. & Guan, Y. Practical Byzantine fault tolerance consensus based on comprehensive reputation. Peer-to-Peer Netw. Appl. 16 (1), 420–430 (2023).

Sajana, P., Sindhu, M. & Sethumadhavan, M. On blockchain applications: Hyperledger fabric and ethereum. Int. J. Pure Appl. Math. 118 (18), 2965–2969 (2018).

Nakandala, S. et al . A tensor compiler for unified machine learning prediction serving. in Proceeding of 14th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2020 , pp. 899–917 (2020).

Alsheikh, M. A., Lin, S., Niyato, D. & Tan, H. P. Machine learning in wireless sensor networks: Algorithms, strategies, and applications. IEEE Commun. Surv. Tutorials 16 (4), 1996–2018 (2014).

Mao, Q., Hu, F. & Hao, Q. Deep learning for intelligent wireless networks: A comprehensive survey. IEEE Commun. Surv. Tutorials 20 (4), 2595–2621 (2018).

Download references

Acknowledgements

“This research work was supported and funded by the National Defence University Malaysia (NDUM)—UPNM/2023/GPPP/ICT/1 and UPNM/2022/GPJP/ICT/3”.

This research received funding from the corresponding research grants: UPNM/2023/GPPP/ICT/1 and UPNM/2022/GPJP/ICT/3.

Author information

Syarifah Bahiyah Rahayu

Present address: Department of Science Defense, Faculty of Defense Science and Technology, National Defence University Malaysia (UPNM), Kuala Lumpur, Malaysia

Authors and Affiliations

Cyber Security & Digital Industrial Revolution Centre, National Defence University Malaysia (UPNM), Kuala Lumpur, Malaysia

K. Venkatesan & Syarifah Bahiyah Rahayu

Department of Science Defense, Faculty of Defense Science and Technology, National Defence University Malaysia (UPNM), Kuala Lumpur, Malaysia

K. Venkatesan

You can also search for this author in PubMed   Google Scholar

Contributions

Dr.K.V: coordinating the work, investigation, methodology, writing original draft, Dr.S.B.R: methodology, project administration, writing, review and editing, All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Syarifah Bahiyah Rahayu .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Venkatesan, K., Rahayu, S.B. Blockchain security enhancement: an approach towards hybrid consensus algorithms and machine learning techniques. Sci Rep 14 , 1149 (2024). https://doi.org/10.1038/s41598-024-51578-7

Download citation

Received : 05 October 2023

Accepted : 07 January 2024

Published : 11 January 2024

DOI : https://doi.org/10.1038/s41598-024-51578-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Mitigating mev attacks with a two-tiered architecture utilizing verifiable decryption.

• Mustafa Ibrahim Alnajjar
• Mehmet Sabir Kiraz
• Suleyman Kardas

EURASIP Journal on Wireless Communications and Networking (2024)

Enhancing network intrusion detection: a dual-ensemble approach with CTGAN-balanced data and weak classifiers

• Mohammad Reza Abbaszadeh Bavil Soflaei
• Arash Salehpour
• Karim Samadzamini

The Journal of Supercomputing (2024)

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Blockchain Security

• First Online: 10 January 2023

Cite this chapter

• Gurdip Kaur 5 ,
• Arash Habibi Lashkari 6 ,
• Iman Sharafaldin 7 &
• Ziba Habibi Lashkari 8  

Part of the book series: Financial Innovation and Technology ((FIT))

387 Accesses

2 Citations

Blockchain is used in various sectors including healthcare, finance, government, and commerce to build blockchain-based solutions for the customers. The main benefit of introducing blockchain in these applications is to provide security to digital transactions by leveraging cryptography, decentralization, and consensus. While the use of blockchain technology has introduced various advantages, it comes up with several cybersecurity challenges as well. Blockchain has attracted cybercriminals to exploit the vulnerabilities that exist in the technology and target organizations that use it.

This chapter sheds light on various blockchain attacks and countermeasures to prevent or avoid those attacks. Blockchain security deals with providing a comprehensive security solution to blockchain applications. It is achieved with the implementation of cybersecurity frameworks, security testing methodologies, and secure coding practices. These countermeasures help protect blockchain solutions from online frauds, breaches, and other cyber-attacks (An Introduction to Blockchain Security. https://www.getastra.com/blog/knowledge-base/blockchain-security/#:~:text=Blockchain%20works%20as%20a%20distributed,for%20data%20storage%20and%20processing) .

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime
• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

The DAO Attacked: Code Issue Leads to $60 Million Ether Theft, https://www.coindesk.com/markets/2016/06/17/the-dao-attacked-code-issue-leads-to-60-million-ether-theft/

Kervins Nicolas, Yi Wang, George C. Giakos, Comprehensive Overview of Selfish Mining and Double Spending Attack Countermeasures, 40th IEEE Sarnoff Symposium, pp. 1-6, 2019.

Google Scholar  

A. Begum, A. H. Tareq, M. Sultana, M. K. Sohel, T. Rahman, and A. H. Sarwar, Blockchain Attacks, Analysis and a Model to Solve Double Spending Attack, International Journal of Machine Learning and Computing, Vol. 10, No. 2, pp. 352-357, 2020.

Arunima Ghosh, Shashank Gupta, Amit Dua, and Neeraj Kumar, Security of Cryptocurrencies in blockchain technology: State-of-art, challenges and future prospects, Journal of Network and Computer Applications, Vol. 163, pp. 1-35, 2020.

Giacomo Morganti, Enrico Schiavone, and Andrea Bondavalli, Risk Assessment of Blockchain Technology, Eighth Latin-American Symposium on Dependable Computing (LADC), pp. 87-96, 2018.

Wenting Li, S´ebastien Andreina, Jens-Matthias Bohli, and Ghassan Karame, Securing Proof-of-Stake Blockchain Protocols, In: Garcia-Alfaro, J., Navarro-Arribas, G., Hartenstein, H., Herrera-Joancomartí, J. (eds) Data Privacy Management, Cryptocurrencies and Blockchain Technology. DPM CBT 2017. Lecture Notes in Computer Science(), Vol 10436. Springer, Cham. https://doi.org/10.1007/978-3-319-67816-0_17 , 2017.

Arunima Ghosh, Shashank Gupta, Amit Dua, and Neeraj Kumar, Security of Cryptocurrencies in blockchain technology: State-of-art, challenges and future prospects, Journal of Network and Computer Applications, Vol. 163, 2020.

Nidhee Rathod and Dilip Motwani, Security threats on Blockchain and its countermeasures, International Research Journal of Engineering and Technology (IRJET), Vol. 05, Issue 11, pp. 1636-1642, 2018.

What is Vector Attack 76?, https://academy.bit2me.com/en/que-es-ataque-de-vector-76/

Xiaoqi Li, Peng Jiang, Ting Chen, Xiapu Luo, and Qiaoyan Wen, A Survey on the Security of Blockchain Systems, Future Generation Computer Systems , Vol. 107, pp. 841-853, 2020.

Khizar Hameed, Mutaz Barika, Saurabh Garg, Muhammad Bilal Amin, Byeong Kang, A taxonomy study on securing Blockchain-based Industrial applications: An overview, application perspectives, requirements, attacks, countermeasures, and open issues, Journal of Industrial Information Integration, Vol 26, 2022

Wenting Li, S´ebastien Andreina, Jens-Matthias Bohli, and Ghassan Karame, Securing Proof-of-Stake Blockchain Protocols, In: Garcia-Alfaro, J., Navarro-Arribas, G., Hartenstein, H., Herrera-Joancomartí, J. (eds) Data Privacy Management, Cryptocurrencies and Blockchain Technology. DPM CBT 2017, Lecture Notes in Computer Science(), Vol. 10436. Springer, Cham. https://doi.org/10.1007/978-3-319-67816-0_17 , 2017.

Kervins Nicolas, Yi Wang, and George C. Giakos, Comprehensive Overview of Selfish Mining and Double Spending Attack Countermeasures, IEEE 40th Sarnoff Symposium, pp. 1-6, doi: 10.1109/Sarnoff47838.2019.9067821, 2019.

Evangelos Deirmentzoglou, Georgios Papakyriakopoulos and Constantinos Patsakis, A Survey on Long-Range Attacks for Proof of Stake Protocols, IEEE Access, Vol. 7, pp. 28712-28725, 2019.

Samiran Bag, Sushmita Ruj, and Kouichi Sakurai, Bitcoin Block Withholding Attack: Analysis and Mitigation, IEEE Transactions on Information Forensics and Security, Vol. 12, No. 8, pp. 1967-1978, 2017.

Yujin Kwon, Dohyun Kim, Yunmok Son, Eugene Vasserman, and Yongdae Kim, Be Selfish and Avoid Dilemmas: Fork After Withholding (FAW) Attacks on Bitcoin, ACM SIGSAC Conference on Computer and Communications Security (CCS), pp. 195-209, 2017.

Congcong Ye, Guoqiang Li, Hongming Cai, Yonggen Gu, and Akira Fukuda, Analysis of Security in Blockchain: Case Study in 51%-Attack Detecting, 5th International Conference on Dependable Systems and Their Applications (DSA), pp. 15-24, 2018.

Feather Forking, KAAN SIMSEK - Medium, https://medium.com/@ksimsek19/feather-forking-f9f3d5b5f9aa , 2021.

Mauro Conti, Sandeep Kumar E, Chhagan Lal, Sushmita Ruj, A survey on security and privacy issues of Bitcoin, https://arxiv.org/abs/1706.00916 , 2017.

Antonio Magnani, Luca Calderoni, and Paolo Palmieri, Feather forking as a positive force: incentivising green energy production in a blockchain-based smart grid, CryBlock'18: Proceedings of the 1st Workshop on Cryptocurrencies and Blockchains for Distributed Systems, pp. 99-104, 2018.

Huru Hasanova, Ui-jun Baek, Mu-gon Shin, Kyunghee Cho, and Myung-Sup Kim, A survey on blockchain cybersecurity vulnerabilities and possible countermeasures, International Journal of Network Management, Vol. 29, Issue 2, pp. 1-36, 2018.

Mauro Conti, Sandeep Kumar, Chhagan Lal, and Sushmita Ruj, A Survey on Security and Privacy Issues of Bitcoin, IEEE Communications Surveys & Tutorials, Vol. 20, No. 4, pp. 3416-3452, 2018.

Patrick McCorry, Siamak F. Shahandashti, and Feng Hao, Refund attacks on Bitcoin’s Payment Protocol, In: Grossklags, J., Preneel, B. (eds) Financial Cryptography and Data Security, Lecture Notes in Computer Science(), Vol 9603. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-54970-4_34 , 2016.

Arthur Gervais, Hubert Ritzdorf, Ghassan O. Karame, and Srdjan Cˇapkun, Tampering with the Delivery of Blocks and Transactions in Bitcoin, CCS '15: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security , pp. 692–705, 2015.

Maria Apostolaki, Aviv Zohar, and Laurent Vanbever, Hijacking Bitcoin: Routing Attacks on Cryptocurrencies, IEEE Symposium on Security and Privacy (SP), pp. 375-392, 2017.

Shijie Zhang and Jong-Hyouk Lee, Double-Spending With a Sybil Attack in the Bitcoin Decentralized Network, IEEE Transactions on Industrial Informatics, Vol. 15, No. 10, pp. 5715-5722, 2019.

Divesh Aggarwal, Gavin K. Brennen, Troy Lee, Miklos Santha, and Marco Tomamichel, Quantum attacks on Bitcoin, and how to protect against them, https://arxiv.org/abs/1710.10377

Download references

Author information

Authors and affiliations.

Saint John, NB, Canada

Gurdip Kaur

School of Information Technology, York University, Toronto, ON, Canada

Arash Habibi Lashkari

Application Security, Forward Security Inc, Vancouver, BC, Canada

Iman Sharafaldin

School of Computer Engineering, Universidad Politécnica de Madrid, Madrid, Spain

Ziba Habibi Lashkari

You can also search for this author in PubMed   Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Kaur, G., Habibi Lashkari, A., Sharafaldin, I., Habibi Lashkari, Z. (2023). Blockchain Security. In: Understanding Cybersecurity Management in Decentralized Finance. Financial Innovation and Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-23340-1_4

Download citation

DOI : https://doi.org/10.1007/978-3-031-23340-1_4

Published : 10 January 2023

Publisher Name : Springer, Cham

Print ISBN : 978-3-031-23339-5

Online ISBN : 978-3-031-23340-1

eBook Packages : Business and Management Business and Management (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

• Conferences
• New Conferences
• search search
• You are not signed in

External Links

• Google Scholar
• References: 0
• Cited by: 0
• Bibliographies: 0
• [Upload PDF for personal use]

Researchr is a web site for finding, collecting, sharing, and reviewing scientific publications, for researchers by researchers.

Sign up for an account to create a profile with publication list, tag and review your related work, and share bibliographies with your co-authors.

Analysis of Security in Blockchain: Case Study in 51%-Attack Detecting

Congcong Ye , Guoqiang Li 0001 , Hongming Cai , Yonggen Gu , Akira Fukuda . Analysis of Security in Blockchain: Case Study in 51%-Attack Detecting . In 5th International Conference on Dependable Systems and Their Applications, DSA 2018, Dalian, China, September 22-23, 2018 . pages 15-24 , IEEE, 2018. [doi]

• Bibliographies

Abstract is missing.

• Web Service API

Analysis of security in blockchain: Case study in 51%-attack detecting

研究成果 : 書籍/レポート タイプへの寄稿 › 会議への寄与

Recently, the global outbreak of a blackmail virus WannaCry, makes the blockchain a hot topic. The security of blockchain is always the focus of people's attention, and it is also the main reason why the blockchain has not been widely used all over the world. Many researches use mathematical derivation method to analyse the 51%-Attacks influence of blockchain, which is very stiff and difficult to understand. In this paper, we propose a method to simulate blockchain's process and discover the rule between attacking method, attacking power and security of blockchain. We take 51%-Attacks as an example and use Java to simulate the running process. By adjusting the value of attacking power, we can get most states of blockchain and analyze the probability that honest state becomes attacking state. We use various forms to analyze and show the experimental result, which verify our method is correct and feasible. This method can also be implemented as a middleware software of blockchain to detect the security of blockchain.

!!!All Science Journal Classification (ASJC) codes

• コンピュータ サイエンスの応用
• 安全性、リスク、信頼性、品質管理
• 10.1109/DSA.2018.00015
• Scopusの出版物のリンク
• Scopusの被引用数のリンク
• Blockchain Computer Science 100%
• Attack Computer Science 30%
• Experimental Result Computer Science 10%
• Probability Computer Science 10%
• Case Study Computer Science 10%
• Running Process Computer Science 10%
• Middleware Software Computer Science 10%

T1 - Analysis of security in blockchain

T2 - 5th International Conference on Dependable Systems and Their Applications, DSA 2018

AU - Ye, Congcong

AU - Li, Guoqiang

AU - Cai, Hongming

AU - Gu, Yonggen

AU - Fukuda, Akira

N1 - Publisher Copyright: © 2018 IEEE.

PY - 2018/12/5

Y1 - 2018/12/5

N2 - Recently, the global outbreak of a blackmail virus WannaCry, makes the blockchain a hot topic. The security of blockchain is always the focus of people's attention, and it is also the main reason why the blockchain has not been widely used all over the world. Many researches use mathematical derivation method to analyse the 51%-Attacks influence of blockchain, which is very stiff and difficult to understand. In this paper, we propose a method to simulate blockchain's process and discover the rule between attacking method, attacking power and security of blockchain. We take 51%-Attacks as an example and use Java to simulate the running process. By adjusting the value of attacking power, we can get most states of blockchain and analyze the probability that honest state becomes attacking state. We use various forms to analyze and show the experimental result, which verify our method is correct and feasible. This method can also be implemented as a middleware software of blockchain to detect the security of blockchain.

AB - Recently, the global outbreak of a blackmail virus WannaCry, makes the blockchain a hot topic. The security of blockchain is always the focus of people's attention, and it is also the main reason why the blockchain has not been widely used all over the world. Many researches use mathematical derivation method to analyse the 51%-Attacks influence of blockchain, which is very stiff and difficult to understand. In this paper, we propose a method to simulate blockchain's process and discover the rule between attacking method, attacking power and security of blockchain. We take 51%-Attacks as an example and use Java to simulate the running process. By adjusting the value of attacking power, we can get most states of blockchain and analyze the probability that honest state becomes attacking state. We use various forms to analyze and show the experimental result, which verify our method is correct and feasible. This method can also be implemented as a middleware software of blockchain to detect the security of blockchain.

UR - http://www.scopus.com/inward/record.url?scp=85060706293&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85060706293&partnerID=8YFLogxK

U2 - 10.1109/DSA.2018.00015

DO - 10.1109/DSA.2018.00015

M3 - Conference contribution

AN - SCOPUS:85060706293

T3 - Proceedings - 2018 5th International Conference on Dependable Systems and Their Applications, DSA 2018

BT - Proceedings - 2018 5th International Conference on Dependable Systems and Their Applications, DSA 2018

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 22 September 2018 through 23 September 2018

IEEE Account

• Change Username/Password
• Update Address

Purchase Details

• Payment Options
• Order History
• View Purchased Documents

Profile Information

• Communications Preferences
• Profession and Education
• Technical Interests
• US & Canada: +1 800 678 4333
• Worldwide: +1 732 981 0060
• Contact & Support
• About IEEE Xplore
• Accessibility
• Terms of Use
• Nondiscrimination Policy
• Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Understanding the 51% Attack: Detection and Prevention Strategies

Published on:

Within the realm of blockchain technology, the notion of a 51% attack has captured considerable interest and evoked apprehensions regarding the security of networks. To effectively combat this malicious act, it is imperative to grasp its intricacies. In this article, we will explore the depths of a 51% attack, providing insights into its definition and emphasizing the criticality of detection and prevention strategies.

Table of Contents

Unveiling the Definition of a 51% Attack

A 51% attack denotes a cyber assault directed at a blockchain network, wherein the assailant acquires dominion over a majority share, exceeding 50%, of the network’s mining power. This control grants them the ability to manipulate the blockchain in various ways, including reversing transactions, executing double-spending of coins, or obstructing the confirmation of new transactions.

These attacks predominantly focus on proof-of-work (PoW) blockchains, where miners engage in a competitive race to solve intricate mathematical puzzles, thereby appending new blocks to the blockchain ledger. By surpassing the 50% threshold of mining power, the attacker gains control over block inclusion, dictating which blocks are accepted and which are rejected.

Although 51% attacks are relatively uncommon, they have proven successful in the past. Notably, in 2014, a group of assailants orchestrated a 51% attack on the Bitcoin Gold network, enabling them to reverse transactions and conduct double-spending. Likewise, in 2016, the Ethereum Classic network fell victim to a similar attack.

Significance of Detecting 51% Attacks

The severity of 51% attacks necessitates the development and implementation of robust detection mechanisms. When an attacker controls over 50% of the network’s mining power, the implications can be detrimental, leading to the following adverse outcomes:

• Double-spending: By allocating coins to multiple recipients simultaneously, the attacker can counterfeit the coins, compromising their integrity. Essentially, the same coins are used to pay for different items, resulting in financial fraud.
• Transaction prevention: With control over the blockchain, the attacker can impede the confirmation of new transactions. This obstruction disrupts businesses that rely on blockchain technology, such as cryptocurrency exchanges and decentralized finance (DeFi) applications, hampering their functionality and causing financial losses.
• Manipulation of the blockchain: A successful 51% attack empowers the attacker to manipulate the blockchain by reversing transactions or adding blocks containing false information. This tampering erodes trust in the blockchain’s reputation, undermining its integrity and potentially causing chaos within the ecosystem.

Given these grave consequences, it is crucial to invest in robust detection and prevention strategies to safeguard blockchain networks.

Unveiling the Mechanics of a 51% Attack

51% attacks predominantly target proof-of-work (PoW) blockchains, where miners compete to solve complex mathematical problems to append blocks to the blockchain. By gaining control over 50% or more of the mining power, the attacker can dictate the fate of block inclusion.

Here’s a closer look at the mechanics of a 51% attack:

• Acquisition of Mining Power Control: The attacker initiates the attack by securing control over more than 50% of the network’s mining power. This can be achieved through purchasing or renting mining equipment or compromising other miners’ computers.
• Manipulation of the Blockchain: With control over the mining power, the attacker begins manipulating the blockchain. They can reverse transactions, execute double-spending, or hinder the confirmation of new transactions.
• Sustaining Dominance and Potential Reversibility: When the attacker possesses over 50% of the mining power, they retain the ability to persistently manipulate the blockchain. Nevertheless, in the event of losing control, the remaining miners can revert the alterations and reinstate the blockchain to its initial state.

51% attacks pose a significant threat to the security of blockchain networks and necessitate proactive measures to mitigate their risks.

Read Also: Solana Saga Crypto Smartphone: A Step Towards Mainstream Adoption

Exploring Consensus Mechanisms: Unveiling the Backbone of Blockchain Networks

To gain a comprehensive understanding of the mechanics underlying a 51% attack, it is crucial to delve into the diverse consensus mechanisms employed by blockchain networks. Consensus mechanisms serve as the foundation for nodes within a blockchain network to reach an agreement on the network’s state. This becomes essential due to the decentralized nature of blockchains, where a central authority is absent to verify transactions or append new blocks.

Within the realm of blockchain technology, there exist diverse consensus mechanisms that govern the validation and agreement of transactions on the network. Two prominent consensus mechanisms are:

Proof-of-Work (PoW)

PoW stands as the prevailing consensus mechanism adopted by Bitcoin and numerous other cryptocurrencies. In this system, miners engage in a competitive race to solve intricate mathematical problems, aiming to add new blocks to the blockchain. Upon successfully solving the computational puzzle, the miner secures a position of distinction and is duly rewarded with a block reward, usually in the form of a specific quantity of cryptocurrency. PoW ensures that the addition of blocks is based on the computational work performed by miners.

Proof-of-Stake (PoS)

PoS represents a newer consensus mechanism that is regarded as more energy-efficient compared to PoW. In a PoS system, nodes are randomly selected to append new blocks to the blockchain based on the quantity of cryptocurrency they have staked. Staking refers to the process of locking up one’s cryptocurrency holdings to participate in the consensus mechanism. PoS reduces the need for intensive computational work, thereby mitigating the environmental impact associated with PoW.

Consensus Mechanisms beyond PoW and PoS

While PoW and PoS are widely known, several other consensus mechanisms exist, each with its unique advantages and disadvantages. These include:

Proof-of-Authority (PoA): In PoA, the consensus is achieved through the reputation and identity of trusted authorities who validate transactions and add blocks to the blockchain.

Proof-of-Burn (PoB): The consensus mechanism known as Proof-of-Burn (PoB) mandates participants to undergo the process of deliberately destroying a specific quantity of cryptocurrency, thereby showcasing their unwavering dedication to the network. Through this burning process, the available supply of cryptocurrency is effectively diminished, emphasizing the commitment of participants and bolstering the network’s security and value proposition.

Delegated Proof-of-Stake (DPoS): DPoS introduces a democratic and participation-driven approach, wherein token holders exercise their voting rights to select a select group of trusted nodes. These nodes, referred to as delegates or witnesses, assume the responsibility of validating transactions and safeguarding the overall security of the network. By empowering token holders to elect delegates, DPoS promotes transparency, decentralization, and efficient consensus, ensuring the integrity and resilience of the blockchain ecosystem.

The choice of consensus mechanism depends on the specific requirements and objectives of a blockchain network. For instance, PoW excels in environments where high security is paramount, while PoS shines in networks striving for energy efficiency.

Comparing PoW and PoS

To better comprehend the disparities between PoW and PoS, consider the following table summarizing their key differences:

By understanding the intricacies of various consensus mechanisms, we can better appreciate the underlying mechanisms susceptible to manipulation in a 51% attack. A comprehensive comprehension of these mechanisms forms the basis for effective detection and prevention strategies to safeguard the integrity and security of blockchain networks.

Exploiting the Majority Control

When an assailant or a collective group obtains control over more than 50% of the mining power in a proof-of-work (PoW) blockchain, they possess the means to exploit their commanding position through various tactics, such as:

• Double-spending: The malefactor can engage in double-spending by transmitting coins to two distinct recipients. This malicious act results in the creation of counterfeit coins, as the same currency is utilized for two separate transactions.
• Transaction Prevention: The attacker has the ability to obstruct the confirmation of new transactions, thereby impeding payments. This disruption can severely impact businesses relying on blockchain technology, including cryptocurrency exchanges and decentralized finance (DeFi) applications.
• Blockchain Manipulation: Through transaction reversals or the addition of fraudulent information to blocks, the attacker possesses the ability to manipulate the integrity of the blockchain. This Blockchain manipulation not only impacts the reputation of the blockchain but also erodes trust in the entire system.

In addition to these forms of attack, an attacker wielding majority control can also execute the following actions:

• Assault on Other Blockchains: If the attacker possesses a substantial amount of hash power, they can direct it towards attacking other blockchains. Such attacks may involve launching denial-of-service attacks or attempting to double-spend coins transferred between blockchains.
• Mining Empty Blocks: The attacker may choose to mine empty blocks, a wasteful practice that consumes the resources of other miners without contributing meaningful transactions or data.
• Alteration of Consensus Rules: The attacker can modify the consensus rules of the blockchain, potentially granting themselves additional power or simplifying the execution of future attacks.

The severity of the consequences resulting from a majority attack depends on the specific characteristics of the targeted blockchain. For instance, if an attacker gains majority control over a blockchain used for storing financial transactions, the repercussions could be dire. However, if the attack occurs on a blockchain-primarily handling non-sensitive data, the implications may be less severe.

Serious Implications of a Successful 51% Attack

A successful 51% attack on a blockchain network carries significant implications, including:

• Double-spending: The attacker can engage in double-spending by sending coins to multiple recipients, effectively creating counterfeit currency.
• Transaction Prevention: The attacker can impede the confirmation of new transactions, leading to payment blockages that disrupt businesses reliant on blockchain technology, such as cryptocurrency exchanges and decentralized finance (DeFi) applications.
• Blockchain Manipulation: By manipulating the blockchain through transaction reversals or the introduction of false information within new blocks, the attacker erodes the reputation of the blockchain and undermines trust in the system.
• Assault on Other Blockchains: An attacker in control of considerable hash power can direct their efforts towards attacking other blockchains, employing denial-of-service attacks or attempting to double-spend coins transferred between chains.
• Alteration of Consensus Rules: The attacker can modify the consensus rules of the blockchain, granting themselves greater power or facilitating future attacks.

In some cases, a successful 51% attack can lead to the complete collapse of the targeted blockchain network. In the event of an attacker acquiring majority control over a cryptocurrency blockchain, they can essentially seize all the coins within the network.

The outcome of a 51% attack is influenced by key factors such as the network’s size, the attacker’s hash power, and the effectiveness of security measures in place.

Detecting a 51% Attack: Unveiling the Threat

The identification of a 51% attack on a blockchain network can be accomplished through various methods, including:

• Monitoring the Hash Rate: The hash rate, quantifying the computational power employed for block mining, serves as a critical metric. An abrupt surge in the hash rate may indicate an attacker’s attempt to seize control of the network.
• Observing the Block Time: The block time signifies the average duration required for mining a new block. A sudden reduction in block time could signify an attacker’s manipulation of the blockchain.
• Tracking Transaction Fees: Transaction fees, the remuneration offered to miners for including transactions within blocks, play a crucial role. A sudden surge in transaction fees may imply an attacker’s endeavor to impede the confirmation of new transactions.
•   Vigilance over the Blockchain: The blockchain, an open ledger recording all network transactions, demands close scrutiny. Any unexpected modifications to the blockchain may suggest an attacker’s manipulation attempts.

Prompt reporting of suspected 51% attacks to blockchain developers is vital for investigation and damage mitigation.

Consider the following additional tips for detecting such attacks:

• Utilize a Blockchain Explorer: Blockchain explorers are websites offering transaction and block visibility. Employing a blockchain explorer can aid in monitoring the network for signs of a 51% attack.
• Stay Informed on Security Threats: Constant vigilance regarding evolving blockchain security threats is imperative. Staying up-to-date facilitates the identification and mitigation of potential attacks.
• Choose a Reputable Blockchain Platform: With numerous blockchain platforms available, selecting one with a strong security reputation is essential.

Signs and Indicators of a Potential 51% Attack

To detect a potential 51% attack, watch out for the following signs and indicators:

• Sudden Surge in Hash Rate: A significant increase in the hash rate indicates a surge in computational power deployed for block mining, potentially signaling an attacker’s attempt to gain control.
• Abrupt Decrease in Block Time: A sudden reduction in block time suggests potential manipulation of the blockchain by an attacker.
•   Unexpected Rise in Transaction Fees: An unanticipated spike in transaction fees hints at an attacker’s intention to hinder the confirmation of new transactions.
• Unexpected Alterations to the Blockchain: Unexpected modifications to the blockchain may signify an attacker’s attempt to manipulate its integrity.
•   Heightened Activity from a Single Mining Pool: A sudden increase in block mining from a single mining pool could suggest their pursuit of network control.
•   Reports of Suspicious Activity: Reports of unusual activities, such as double-spends or transaction reversals, should raise concerns and indicate the possibility of an ongoing 51% attack.

Reporting any signs or indicators to blockchain developers is crucial for prompt investigation and mitigation measures. Additionally, maintaining awareness of the latest security threats through security blogs, websites, and reports empowers proactive identification and defense against potential attacks.

Network Analysis: Unveiling 51% Attack Patterns

Detecting a 51% attack requires a meticulous examination of the blockchain network, delving into transaction flows, node performance, and the integrity of block confirmations. By scrutinizing these aspects, one can uncover anomalous patterns or deviations from the expected norm, potentially revealing an attacker’s quest for network control.

Vigilance over Mining Pool Activity

Mining pools, wielding considerable hash power on numerous blockchain networks, become prime targets for 51% attacks. By carefully monitoring mining pool activities, one can detect early warning signs of an impending attack, such as sudden shifts in dominance, disproportionately high mining rewards, or orchestrated actions among miners. These warning signals play a vital role in fortifying the network against attacks and minimizing resultant damage.

Risk Assessment for Blockchain Networks

Several factors render a blockchain network more susceptible to a 51% attack, including:

• Hash power distribution: Networks, where a small number of entities command a substantial portion of the hash power face, heightened vulnerability to 51% attacks.
• Attack cost: The feasibility of attacking a blockchain network is determined by the required hash power and associated costs. Networks with low attack costs are more prone to being targeted.
• Network security: A blockchain network’s security hinges on its design and the implementation of its consensus algorithm. Inadequate security measures render networks more susceptible to 51% attacks.

Specific Vulnerability Factors

Certain factors amplify a blockchain network’s vulnerability to a 51% attack:

• Reliance on Proof-of-Work (PoW): PoW, the prevailing consensus algorithm, exposes networks to 51% attacks due to miners’ competition to solve mathematical puzzles for block addition. When an attacker controls the majority of hash power in a PoW network, they gain control over the network and obstruct legitimate transaction confirmations.
• Lack of mining pool diversity: Networks with a limited number of dominant mining pools face heightened vulnerability to 51% attacks. In such cases, attackers can simply incentivize these pools to mine on their behalf.
• Weak community engagement: Blockchain networks lacking an active and vigilant community become more susceptible to 51% attacks. A strong community serves as a monitoring force, promptly reporting suspicious activities. Without an active community, attackers can execute 51% attacks undetected.

Assessing the Impact of a 51% Attack

The consequences of a 51% attack vary depending on the targeted blockchain network and the attacker’s motives. Potential impacts may include:

• Double-spending: Attackers exploit double-spending by mining blocks that duplicate cryptocurrency transactions, allowing them to spend the same currency twice.
• Transaction confirmation prevention: Attackers can obstruct transaction confirmations by selectively mining blocks that exclude desired transactions, effectively impeding legitimate users from utilizing the blockchain network.
• Blockchain history manipulation: Attackers can rewrite the blockchain’s history by mining blocks with altered transactions compared to legitimate blocks. This technique enables transaction reversals, cryptocurrency theft, and content censorship.
• Network disruption: Attackers can disrupt the network by flooding it with invalid transactions or targeting network nodes. Such actions hinder user functionality and tarnish the network’s reputation.

Factors Influencing 51% Attacks

Blockchain networks that fall under the following categories are more prone to 51% attacks:

• Small Networks: Smaller networks possess less hash power and a smaller community, making them attractive targets for attacks.
• New Networks: Nascent blockchain networks lack established foundations and robust security measures, rendering them susceptible to 51% attacks.
• Vulnerable Networks: Networks vulnerable to 51% attacks are particularly at risk. This includes networks utilizing Proof-of-Work (PoW) consensus algorithms, exhibiting a lack of mining pool diversity, or having a weak community.

Several notable blockchain networks have experienced 51% attacks, resulting in significant losses:

• Bitcoin Gold: In 2018, Bitcoin Gold suffered a 51% attack that led to the theft of $18 million worth of cryptocurrency.
• Verge: Verge encountered two attacks in 2018, leading to the theft of $1.75 million worth of cryptocurrency.
• Equihash-based Blockchains: Equihash, a vulnerable Proof-of-Work consensus algorithm, has been exploited in various attacks on blockchains such as Zcash, Bitcoin Private, and Horizen.
• Proof-of-Stake blockchains: While Proof-of-Stake (PoS) blockchains are less vulnerable to 51% attacks than Proof-of-Work (PoW) blockchains, they are not immune. In 2020, the PoS blockchain EOS was attacked, resulting in the theft of $5 million worth of cryptocurrency.

It is essential to recognize that any blockchain network, irrespective of size, age, or consensus algorithm, can potentially fall victim to a 51% attack. However, the networks mentioned above are more likely targets due to their heightened vulnerability.

Fortifying Consensus Mechanisms: Prevention Strategies

Various approaches can be used to strengthen consensus mechanisms in blockchain networks, including:

• Diversification of the Mining Pool: A diverse array of miners, each controlling a smaller fraction of the hash power, makes it difficult for an attacker to gain control over the network.
• Adoption of Alternative Consensus Algorithms: Choosing consensus algorithms that exhibit reduced vulnerability to 51% attacks, such as certain Proof-of-Stake (PoS) networks, can enhance network security by eliminating the need for miners to compete through mathematical puzzles.
• Cultivating a Strong Community: A robust and active community plays a pivotal role in monitoring the network and promptly reporting any suspicious activities, bolstering the network’s ability to detect and mitigate 51% attacks.
• Implementation of Checkpointing Systems: Checkpointing systems ensure the immutability of the blockchain by creating regular checkpoints stored on separate networks. Attempting to rewrite the blockchain would necessitate tampering with the checkpoints, thereby increasing the difficulty for attackers.
• Adoption of Hybrid Consensus Mechanisms: By integrating two or more distinct consensus algorithms, hybrid consensus mechanisms reinforce network security, making it more challenging for attackers to seize control.

Additional Strategies for Consensus Mechanism Strengthening:

• Utilization of Proof-of-Stake (PoS) Consensus Algorithms: PoS algorithms mitigate vulnerability to 51% attacks by relying on validators who secure the network through cryptocurrency staking, reducing the need for competitive mining.
• Deployment of Delegated Proof-of-Stake (DPoS) Consensus Algorithms: DPoS variations elect validators through token holder voting, making it more difficult for attackers to control the network without a majority stake.
• Adoption of Slashing Mechanisms: Slashing mechanisms penalize validators engaging in malicious behavior, acting as a deterrent against attacks on the network.
• Integration of Byzantine Fault Tolerant (BFT) Consensus Algorithms: BFT algorithms, designed to withstand malicious actions, offer ideal security for blockchain networks demanding heightened protection.

By employing these strategies, blockchain networks can fortify their consensus mechanisms and elevate resilience against 51% attacks.

Enhancing Network Security: Key Measures

To safeguard against 51% attacks and other security threats, it is crucial to focus on network security through the following actions:

• Diversify the Mining Pool: Cultivating a broad range of miners, each possessing a smaller proportion of the hash power, complicates an attacker’s control over the network.
• Utilize Different Consensus Algorithms: Consensus algorithms that exhibit reduced vulnerability to 51% attacks, such as Proof-of-Stake (PoS) networks, mitigate risks by eliminating the need for competitive mining.
• Foster a Strong Community: An engaged and vigilant community aids in monitoring the network, promptly reporting suspicious activities, and detecting and mitigating potential 51% attacks.
• Stay Informed on Security Threats: Regularly update your knowledge by accessing resources such as blockchain project websites, security blogs, and firms specializing in security to stay abreast of the latest security threats.
• Implement a Firewall: Utilize a firewall to protect your network from unauthorized access and external threats.
• Use a VPN: Enhance security by encrypting network traffic and safeguarding privacy through the use of a Virtual Private Network (VPN).
• Keep Software Up to Date: Regularly update software to incorporate the latest security patches, reinforcing network protection against potential attacks.
• Beware of Phishing Attacks: Educate yourself about the signs of phishing attacks and exercise caution when encountering suspicious emails or messages. Refrain from clicking on links or opening attachments from unknown senders.

By following these measures, network administrators can fortify their systems against 51% attacks and other security risks, bolstering the integrity of their blockchain networks.

Educating network participants about 51% attacks

Educating network participants about the perils of 51% attacks holds paramount importance as a prevention strategy. By instilling awareness of the risks associated with these attacks, participants can heighten their vigilance when monitoring network activities and implement measures to shield themselves from exploitation by malicious actors.

Various methods can be employed to educate network participants about 51% attacks, including the following:

• Provision of comprehensive educational resources: Curating informative articles, blog posts, or videos elucidating the intricacies of 51% attacks, their modus operandi, and effective defense mechanisms.
• Organization of educational events: Facilitating webinars, meetups, or conferences that unite network participants in knowledge-sharing endeavors, enlightening them about the nuances of 51% attacks.
• Integration of educational content into user experiences: Embedding information pertaining to 51% attacks within the user interface of wallets or exchanges, ensuring participants are informed and prepared.

With the dissemination of knowledge about 51% attacks to network participants, the network’s overall security can be bolstered, effectively fortifying against these threats.

Supplementary tips for educating network participants about 51% attacks encompass the following:

• Utilize clear and concise language: Craft educational resources that are easily comprehensible, employing unambiguous terminology to elucidate complex concepts effectively.
• Emphasize practical implications: Convey the potential ramifications of 51% attacks on users and elucidate actionable steps they can take to safeguard themselves from such threats.
• Leverage real-world examples: Cite historical instances of 51% attacks to illustrate the gravity of the risks involved, thereby heightening participants’ understanding.
• Foster engagement: Incorporate interactive elements and visually stimulating components within educational resources to enhance participant engagement and retention.

By adhering to these guidelines, educational resources can be created that equip network participants with a comprehensive understanding of the risks posed by 51% attacks and empower them to proactively safeguard their interests.

Encouraging Decentralization to thwart 51% attacks

Encouraging decentralization stands as a pivotal approach to mitigate the susceptibility of blockchain networks to 51% attacks. Decentralization entails the dispersion of power and control across a network of nodes, rendering it arduous for any single entity to gain dominance and effectively safeguarding against such attacks.

Multiple strategies can be implemented to foster decentralization within blockchain networks, including:

• Adoption of proof-of-stake (PoS) consensus algorithms: PoS algorithms exhibit reduced vulnerability to 51% attacks compared to proof-of-work (PoW) algorithms. Unlike PoW, PoS does not necessitate miners competing to solve computational puzzles. Instead, validators stake their cryptocurrency to secure the network.
• Expansion of the node network: Increasing the number of nodes within a network raises the difficulty for attackers to seize control and manipulate network operations.
• Diversification of the mining pool: Promoting a wide array of miners, each controlling a small portion of the network’s hash power, creates a formidable challenge for potential attackers seeking control.
• Cultivation of a robust community: An engaged community plays a pivotal role in monitoring network activities, promptly reporting suspicious behavior, and collectively safeguarding the integrity of the network.

By fostering decentralization in blockchain networks, several additional benefits can be realized:

• Resilience: Decentralized networks exhibit enhanced resilience against attacks as they lack a single point of failure, mitigating the impact of any potential breaches.
• Transparency: Compared to centralized networks, decentralized networks offer heightened transparency. All transactions are recorded on the blockchain, a publicly accessible ledger.
• Efficiency: Decentralized networks can surpass centralized networks in terms of efficiency. The absence of a central authority’s approval streamlines transaction processing.

By advocating for decentralization, blockchain networks can be fortified, ensuring heightened security, resilience, transparency, and efficiency in the face of potential 51% attacks.

Emergency Response Plans for Mitigating 51% Attacks

To safeguard blockchain networks, establishing comprehensive emergency response plans for potential 51% attacks is of paramount importance. These plans enable network operators to proactively prepare for and effectively respond to such attacks, thereby minimizing the resultant damage.

Key components that should be incorporated into an emergency response plan for 51% attacks include:

• Identifying the Key Stakeholders: The initial step involves identifying the crucial stakeholders who should be involved in the response plan. This encompasses network operators, miners, exchanges, and users, ensuring comprehensive collaboration in addressing an attack.
• Defining Roles and Responsibilities: Once the key stakeholders are identified, it is imperative to delineate their specific roles and responsibilities within the response plan. This clarity ensures a coordinated and efficient response in the face of an attack.
• Developing a Communication Plan: A well-structured communication plan is vital for keeping stakeholders informed about the attack and the response strategy. This plan should outline the methods and timelines for notifying stakeholders and establishing effective inter-stakeholder communication channels.
• Identifying Mitigation Strategies: The response plan must also outline mitigation strategies to minimize the impact of an attack. These strategies may include temporary network suspension, blockchain rollbacks, or issuing refunds to affected users.
• Testing the Plan: To ensure its efficacy, the response plan should undergo rigorous testing. Simulating an attack scenario allows for the assessment of the plan’s implementation and identification of potential weaknesses or areas for improvement.

By diligently following these steps, a robust emergency response plan can be developed, effectively fortifying blockchain networks against the risks associated with 51% attacks.

Consider these additional tips when establishing emergency response plans for potential 51% attacks:

• Keep the Plan Updated: Regularly reviewing and updating the plan guarantees its relevance and effectiveness during an actual attack.
• Communicate the Plan to Stakeholders: Transparently communicating the response plan to all stakeholders ensures that everyone is aware of their respective roles and knows how to respond in the event of an attack.
• Regularly Test the Plan: Consistent testing of the plan guarantees its functionality and identifies any areas that require improvement, ensuring a swift and efficient response when necessary.

By adhering to these tips, a robust emergency response plan can be developed, empowering blockchain network operators to effectively protect their systems from the risks posed by 51% attacks.

Case Studies: Noteworthy 51% Attacks

Throughout the history of blockchain, numerous notable 51% attacks have occurred, underscoring the real and substantial threat they pose to network security. Some noteworthy examples include:

Verge (XVG): In 2018, an attacker gained control of over 50% of the hash power on the Verge network. Exploiting this dominance, the attacker executed double-spending attacks and compromised the network’s infrastructure.

Expanse (EXP): In 2019, an attacker gained control of more than 50% of the hash power on the Expanse network. This control was exploited to carry out double-spending attacks and undermine the network’s infrastructure.

Monacoin (MONA): In 2019, an attacker successfully assumed control of over 50% of the hash power on the Monacoin network. Utilizing this power, the attacker executed double-spending attacks and targeted the network’s infrastructure.

Nervos Network (CKB): In 2020, an attacker gained control of more than 50% of the hash power on the Nervos Network, enabling them to execute double-spending attacks and compromise the network’s infrastructure.

These instances exemplify the gravity of 51% attacks, highlighting the need for robust preventative measures and effective emergency response plans to safeguard blockchain networks from such threats.

Collaboration and Information Sharing for Mitigating 51% Attacks

Collaboration and the exchange of information are pivotal in the battle against 51% attacks within the blockchain community. By promoting open communication and sharing insights, stakeholders can collectively work to mitigate the risks associated with these attacks.

Importance of Open Communication

Creating platforms and communities where blockchain enthusiasts, developers, and security experts can freely share knowledge and experiences is crucial. Such open communication channels facilitate the dissemination of information and the swift identification of emerging threats.

Sharing Prevention and Detection Insights

Active sharing of insights, best practices, and innovative prevention techniques strengthens the collective security posture. Through collaboration, stakeholders can continuously refine their strategies for detecting and preventing 51% attacks, remaining one step ahead of potential threats.

Collaborative Efforts in Risk Mitigation

Collective initiatives, including bug bounty programs, joint research projects, and coordinated response frameworks, foster collaboration among blockchain networks, security researchers, and developers. By pooling resources and expertise, stakeholders can effectively mitigate the risks associated with 51% attacks.

Regulatory Considerations for Addressing Risks

As the blockchain ecosystem evolves, regulatory interventions and frameworks are emerging to address the risks posed by 51% attacks. However, striking the right balance between security and decentralization presents a challenge.

Government Interventions and Regulations

Governments worldwide are acknowledging the significance of blockchain technology and its vulnerabilities, leading to the development of regulatory frameworks that promote secure practices without stifling innovation. Finding this delicate balance is a crucial task for regulators.

Approaches to Regulation

Different countries adopt varying approaches to regulate blockchain technology. Some, like China, lean toward more restrictive measures, while others, like the United States, opt for minimal regulation to foster industry growth.

Challenges for Regulatory Frameworks

Developing regulatory frameworks for blockchain technology poses several challenges:

Evolving Technology: Blockchain technology is still in its early stages, making it challenging for regulators to develop future-proof frameworks.

Global Nature: Blockchain networks transcend national borders, necessitating international cooperation among regulators.

Complexity: The intricate nature of blockchain technology can make it difficult for regulators to grasp associated risks and vulnerabilities.

Opportunities for Effective Regulation

Regulators can develop frameworks that promote secure practices without stifling innovation by:

Focusing on Risks: Addressing risks like fraud, money laundering, and market manipulation through well-targeted frameworks.

Encouraging Innovation: Promoting an environment that fosters innovation in the blockchain industry, avoiding excessive restrictions.

Collaboration with Industry: Engaging with industry stakeholders to ensure effective and industry-supported regulatory frameworks.

Balancing Security and Decentralization

Regulatory efforts must carefully weigh the trade-offs between security and decentralization. While stricter regulations enhance security, they should not compromise the core principles of blockchain technology, such as decentralization and user autonomy.

Challenges and Opportunities Ahead

Developing effective regulatory frameworks to combat 51% attacks requires a comprehensive understanding of the technology and its evolving landscape. Policymakers and regulators face the challenge of crafting frameworks that encourage innovation, protect users, and safeguard the integrity of blockchain networks.

Emerging Trends and Technological Progress

The rapid evolution of blockchain technology introduces novel trends and advancements that have the potential to shape the landscape of 51% attack prevention and detection. Ongoing research and development endeavors are devoted to crafting consensus mechanisms that are more resilient and efficient. Innovative approaches such as Proof of Authority (PoA), Delegated Proof of Stake (DPoS), and other consensus algorithms aim to overcome the limitations of existing models while enhancing network security.

Another area of focus lies in the impact of sharding, a technique that divides blockchain data, and scalability solutions like layer-two protocols. These advancements hold promise for bolstering network security by distributing the computational load and increasing transaction throughput, all while upholding robust security measures.

Furthermore, the advent of quantum computers poses a significant threat to the security of blockchain networks. Many of the encryption algorithms currently employed are vulnerable to attacks from these formidable machines. Consequently, blockchain developers must embark on exploring and cultivating quantum-resistant cryptography. This form of cryptography is specifically designed to withstand attacks from quantum computers. Various quantum-resistant cryptography algorithms have been proposed, and researchers are continually developing new ones.

The development of quantum-resistant cryptography is a pivotal area of research in blockchain security. As the power of quantum computers escalates, the need for encryption algorithms that can withstand quantum attacks becomes increasingly imperative.

Some of the most promising quantum-resistant cryptography algorithms include lattice-based cryptography , which relies on the complexity of certain problems in lattice theory that are believed to be insurmountable even for quantum computers. Hash-based cryptography operates on the difficulty of reversing specific hash functions, which are thought to be resistant to reversal even for quantum computers. Code-based cryptography is based on the complexity of decoding particular codes that are considered challenging to decipher, even for quantum computers.

Quantum-resistant cryptography offers a range of benefits, including heightened security against quantum computer-based hacks, scalability to accommodate large-scale applications, and performance optimization without significant impacts on application speed.

Nonetheless, challenges accompany the implementation of quantum-resistant cryptography. It entails greater complexity compared to traditional cryptography, potentially higher costs for implementation, and limited availability at present.

Despite these challenges, the pursuit of quantum-resistant cryptography remains a crucial area of research. As quantum computers gain potency, the need for encryption algorithms that can effectively repel quantum attacks will only intensify.

Conclusion:

comprehending and effectively mitigating the risks posed by 51% attacks are imperative for preserving the integrity and trust within blockchain networks. By understanding the mechanics of such attacks, recognizing early warning signs, implementing prevention strategies, fostering community collaboration, and considering regulatory frameworks, stakeholders can collectively fortify the security of blockchain technology. Sustained efforts to stay ahead of evolving threats will pave the way for a resilient and robust blockchain ecosystem that can fully unleash its potential.

• What is 51% Attack?

How to Choose the Best Crypto PR Firms for Promoting Your Blockchain Project

5 best bitcoin etfs & where to buy in 2024, investing in bitcoin etfs: a comprehensive guide for beginners, the ripple effect: how the approval of bitcoin etf could forever reshape the cryptocurrency landscape, the launch of bitcoin etfs: a new era for crypto investing, the 10 best blockchain courses and certifications for career advancement in 2024, how ai and blockchain integration enhances gaming security, leave a reply cancel reply.

Save my name, email, and website in this browser for the next time I comment.

Stay in the Loop

Get the BchainMeta weekly update, which makes reading the news enjoyable. Join our mailing list for free to stay informed.

Web3 Blockchain Technology Relvolution

Latest Article

A comprehensive guide to iota cryptocurrency: how it works, buying, storing and the future, quick access.

• Privacy Policy
• Cookie Policy
• Terms and Conditions

Disclaimer: The content provided by BchainMeta aims to offer informative insights and should not be construed as investment advice. Engaging in the trading, purchasing, or selling of cryptocurrencies carries inherent risks and readers are strongly urged to conduct their own research prior to making any investment choices. Always DYOR!

© 2023 BchainMeta - All rights reserved.

A Survey of Ethereum Smart Contract Security: Attacks and Detection

New citation alert added.

This alert has been successfully added and will be sent to:

You will be notified whenever a record that you have chosen has been cited.

To manage your alert preferences, click on the button below.

New Citation Alert!

Please log in to your account

Information & Contributors

Bibliometrics & citations, view options, 1 introduction, 2 background, 2.1 blockchain system, 2.2 ethereum, 2.3 smart contract, 3 smart contract vulnerabilities.

3.1 Timestamp Dependency

3.2 transaction-ordering dependency, 3.3 reentrancy.

3.4 Short Address

3.5 mishandled exceptions, 3.6 integer overflow/underflow, 3.7 dangerous delegatecall.

3.8 Frozen Ether

3.9 tx.origin, 3.10 denial of service, 3.11 uninitialized pointer, 4 vulnerability detection tool.

4.1 Symbolic Execution

4.1.1 oyente..

4.1.2 Manticore.

4.1.3 Osiris.

4.1.4 mythril., 4.1.5 maian..

4.2 Formal Verification

4.2.1 zeus., 4.2.2 securify., 4.2.3 vandal..

4.3 Fuzzing

4.3.1 contractfuzzer..

4.3.2 ReGuard.

4.3.3 gasfuzzer., 4.3.4 harvey., 4.4.1 slither..

4.4.2 SmartCheck.

5 challenges, 5.1 challenges in symbol execution, 5.2 challenges in formal verification, 5.3 challenges in fuzzing, 6 conclusion, index terms.

General and reference

Document types

Surveys and overviews

Security and privacy

Systems security

Distributed systems security

Vulnerability management

Vulnerability scanners

Recommendations

Smart contract: a survey towards extortionate vulnerability detection and security enhancement.

Smart contracts become significant as blockchain technology is blooming around the technological globe. In this juncture, smart contract is an automatic programming framework executed based upon pre-conditions in blockchain even among untrusted ...

Security Vulnerabilities in Ethereum Smart Contracts

Smart contracts (SC) are one of the most appealing features of blockchain technologies facilitating, executing, and enforcing predefined terms of coded contracts without intermediaries. The steady adoption of smart contracts on the Ethereum blockchain ...

A survey on smart contract vulnerabilities: Data sources, detection and repair

Smart contracts contain many built-in security features, such as non-immutability once being deployed and non-involvement of third parties for contract execution. These features reduce security risks and enhance users’ trust towards ...

Information

Published in.

• Jairo Gutierrez ,
• Amarolinda Klein ,
• Patrick C. K. Hung

Association for Computing Machinery

New York, NY, United States

Publication History

Check for updates, author tags.

• Ethereum smart contract
• vulnerability detection
• Research-article

Contributors

Other metrics, bibliometrics, article metrics.

• 0 Total Citations
• 539 Total Downloads
• Downloads (Last 12 months) 539
• Downloads (Last 6 weeks) 83

View options

View or Download as a PDF file.

View online with eReader .

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Share this publication link.

Copying failed.

Share on social media

Affiliations, export citations.

• Please download or close your previous search result export first before starting a new bulk export. Preview is not available. By clicking download, a status dialog will open to start the export process. The process may take a few minutes but once it finishes a file will be downloadable from your browser. You may continue to browse the DL while the export process is in progress. Download
• Download citation
• Copy citation

We are preparing your search results for download ...

We will inform you here when the file is ready.

Your file of search results citations is now ready.

Your search export query has expired. Please try again.

• Live Events
• Certification Programs

Case Study – The Verge 51% Attack and Blockchain Consensus Vulnerabilities

Bryant Nielson | August 8, 2023

In April 2018, the Verge blockchain fell victim to a damaging 51% attack. Within hours, threat actors took over Verge’s proof-of-work consensus mechanism and successfully double spent coins, stealing an estimated $1.7 million worth of XVG. This case study will examine how Verge’s consensus was compromised and key lessons learned for bolstering the security of blockchain consensus algorithms.

Verge relied on five mining algorithms for its proof-of-work – Scrypt, X17, Lyra2rev2, myr-groestl and blake2s. This was intended to promote egalitarian access to mining. However, the hybrid approach had a fatal flaw. Rather than requiring miners to distribute hash power evenly across all five algorithms, Verge allowed any single algorithm to meet the hash rate threshold for confirming blocks.

Attackers exploited this by heavily targeting just the Scrypt algorithm, renting mining rigs to acquire over 50% control of Scrypt hash rate. This gave them de facto control of the overall network consensus. The attack proceeded in blocks occurring between block heights 1,560,000 and 1,560,720.

With majority control, the attackers were able to double spend transactions. They first sent XVG to exchanges and swapped for Bitcoin. After exchanges confirmed the deposits, the attackers then forked the Verge chain to erase the transactions. This rolled back the blockchain before the XVG deposits as if they never happened. But the stolen Bitcoin were retained.

Beyond proving vulnerabilities in Verge’s implementation, the attack holds broader lessons for blockchain consensus security. Relying on just one or two mining algorithms centralizes control in the hands of those able to amass hash rate. Multi-algorithm mining should require distribution to preventtakeover by an algorithm subset.

The importance of mining power distribution also applies to PoW networks like Bitcoin and Ethereum. Dominance of mining pools like AntPool must be monitored for over-centralization risks.

For PoS consensus, sufficient staking decentralization and continuous randomization in validator selection prevent takeover by mega-stakers. Frequent random shuffles of validators disrupt any attempt to collude.

Finally, swift response to attacks-in-progress can mitigate losses by identifying the malicious fork and coordinating exchanges to increase confirmations or freeze potentially double-spent deposits before clearing.

By studying past consensus failures like Verge, blockchain architects can design systems resilient to 51% and double spend exploits from day one. With vigilance and an emphasis on true decentralization, both PoW and PoS platforms can confidently grow while keeping community assets secure.

Global cooperation with higher education, associations, government, and industry provides an ecosystem designed to scale knowledge, verified talent, and ultimately innovation. We are open for training, partnership, free industry event hosting, and your questions.

9 AM to 5 PM EST, Monday – Friday 415-301-4000

Administrative Support [email protected]

Learner Support [email protected]

Sales and Partnership

415-301-4000

For program questions, group enrollment, or custom programs please contact: [email protected]

Education, association, and workforce partnerships [email protected]

Mailing Address:

1300 first street – suite 368, napa, ca 94559.

" (required) " indicates required fields

Join our Mailing list!

Get all latest news, exclusive deals and academy updates.

Download Free Report

Download course syllabus.

What is a 51% attack and how to detect it?

Despite the inordinate amount of resources needed to engineer them, small-cap cryptocurrencies are still susceptible to a 51% attacks.

Own this piece of crypto history

Despite being underpinned by blockchain technology that promises security, immutability, and complete transparency, many cryptocurrencies like Bitcoin SV ( BSV ), Litecoin Cash (LCC) and Ethereum Classic ( ETC ) have been subject to 51% attacks several times in the past. While there are many mechanisms by which malicious entities can and have exploited blockchains, a 51% attack, or a majority attack as it is also called, occurs when a group of miners or an entity controls more than 50% of the blockchain’s hashing power and then assumes control over it. 

Arguably the most expensive and tedious method to compromise a blockchain, 51% of attacks have been largely successful with smaller networks that require lower hashing power to overcome the majority of nodes.

Understanding a 51% attack 

Before delving into the technique involved in a 51% attack, it is important to understand how blockchains record transactions , validate them and the different controls embedded in their architecture to prevent any alteration. Employing cryptographic techniques to connect subsequent blocks, which themselves are records of transactions that have taken place on the network, a blockchain adopts one of two types of consensus mechanisms to validate every transaction through its network of nodes and record them permanently.

While nodes in a proof-of-work (PoW) blockchain need to solve complex mathematical puzzles in order to verify transactions and add them to the blockchain, a proof-of-stake (PoS) blockchain requires nodes to stake a certain amount of the native token to earn validator status. Either way, a 51% attack can be orchestrated by controlling the network’s mining hash rate or by commanding more than 50% of the staked tokens in the blockchain.

To understand how a 51% attack works, imagine if more than 50% of all the nodes that perform these validating functions conspire together to introduce a different version of the blockchain or execute a denial-of-service (DOS) attack. The latter is a type of 51% attack in which the remaining nodes are prevented from performing their functions while the attacking nodes go about adding new transactions to the blockchain or erasing old ones. In either case, the attackers could potentially reverse transactions and even double-spend the native crypto token, which is akin to creating counterfeit currency.

Needless to say, such a 51% attack can compromise the entire network and indirectly cause great losses for investors who hold the native token. Even though creating an altered version of the original blockchain requires a phenomenally large amount of computing power or staked cryptocurrency in the case of large blockchains like Bitcoin or Ethereum , it isn’t as far-fetched for smaller blockchains. 

Even a DOS attack is capable of paralyzing the blockchain’s functioning and can negatively impact the underlying cryptocurrency’s price. However, it is improbable that older transactions beyond a certain cut-off can be reversed and thus puts only the most recent or future transactions made on the network at risk.

Is a 51% attack on Bitcoin possible?

For a PoW blockchain, the probability of a 51% attack decreases as the hashing power or the computational power utilized per second for mining increases. In the case of the Bitcoin ( BTC ) network, perpetrators would need to control more than half of the Bitcoin hash rate that currently stands at ~290 exahashes/s hashing power, requiring them to gain access to at least a 1.3 million of the most powerful application-specific integrated circuit (ASIC) miners like Bitmain’s Antminer S19 Pro that retails for around $3,700 each. 

This would entail that attackers need to purchase mining equipment totaling around $10 billion just to stand a chance to execute a 51% attack on the Bitcoin network. Then there are other aspects like electricity costs and the fact that they would not be entitled to any of the mining rewards applicable for honest nodes. 

However, for smaller blockchains like Bitcoin SV , the scenario is quite different, as the network’s hash rate stands at around 590PH/s, making the Bitcoin network almost 500 times more powerful than Bitcoin SV.

In the case of a PoS blockchain like Ethereum, though, malicious entities would need to have more than half of the total Ether ( ETH ) tokens that are locked up in staking contracts on the network. This would require billions of dollars only in terms of purchasing the requisite computing power to even have some semblance of launching a successful 51% attack. 

Moreover, in the scenario that the attack fails, all of the staked tokens could be confiscated or locked, dealing a hefty financial blow to the entities involved in the purported attack.

How to detect and prevent a 51% attack on a blockchain?

The first check for any blockchain would be to ensure that no single entity, group of miners or even a mining pool controls more than 50% of the network’s mining hashrate or the total number of staked tokens. 

This requires blockchains to keep a constant check on the entities involved in the mining or staking process and take remedial action in case of a breach. Unfortunately, the Bitcoin Gold (BTG) blockchain couldn’t anticipate or prevent this from happening in May 2018, with a similar attack repeating in January 2020 that lead to nearly $70,000 worth of BTG being double-spent by an unknown actor. 

In all these instances, the 51% attack was made possible by a single network attacker gaining control over more than 50% of the hashing power and then proceeding to conduct deep reorganizations of the original blockchain that reversed completed transactions.

The repeated attacks on Bitcoin Gold do point out the importance of relying on ASIC miners instead of cheaper GPU-based mining. Since Bitcoin Gold uses the Zhash algorithm that makes mining possible even on consumer graphics cards, attackers can afford to launch a 51% attack on its network without needing to invest heavily in the more expensive ASIC miners. 

This 51% attack example does highlight the superior security controls offered by ASIC miners as they need a higher quantum of investment to procure them and are built specifically for a particular blockchain, making them useless for mining or attacking other blockchains.

However, in the event that miners of cryptocurrencies like BTC shift to smaller altcoins, even a small number of them could potentially control more than 50% of the altcoin’s smaller network hashrate. 

Moreover, with service providers such as NiceHash allowing people to rent hashing power for speculative crypto mining, the costs of launching a 51% attack can be drastically reduced. This has drawn attention to the need for real-time monitoring of chain reorganizations on blockchains to highlight an ongoing 51% attack. 

MIT Media Lab’s Digital Currency Initiative (DCI) is one such initiative that has built a system to actively monitor a number of PoW blockchains and their cryptocurrencies, reporting any suspicious transactions that may have double-spent the native token during a 51% attack.

Cryptocurrencies such as Hanacoin (HANA), Vertcoin (VTC), Verge (XVG), Expanse (EXP), and Litecoin Cash are just a few examples of blockchain platforms that faced a 51% attack as reported by the DCI initiative. 

Of them, the Litecoin Cash attack in July 2019 is a classic example of a 51% attack on a proof-of-stake blockchain, even though the attackers did not mine any new blocks and double-spent Litecoin Cash (LCC) tokens that were worth less than $5,000 at the time of the attack. 

This does highlight the lower risks of 51% attacks on PoS blockchains, deeming them less attractive to network attackers, and is one of the many reasons for an increasing number of networks switching over to the PoS consensus mechanism.

• # Blockchain
• # Proof-of-Stake
• # Transactions
• # Proof-of-Work
• # Bitcoin SV

• Open access
• Published: 02 May 2023

Blockchain-oriented approach for detecting cyber-attack transactions

• Zhiqi Feng 1 ,
• Yongli Li   ORCID: orcid.org/0000-0002-1979-9057 1 &
• Xiaochen Ma 1  

Financial Innovation volume  9 , Article number:  81 ( 2023 ) Cite this article

3557 Accesses

3 Citations

Metrics details

With the high-speed development of decentralized applications, account-based blockchain platforms have become a hotbed of various financial scams and hacks due to their anonymity and high financial value. Financial security has become a top priority with the sustainable development of blockchain-based platforms because of an increasing number of cyber attacks, which have resulted in a huge loss of crypto assets in recent years. Therefore, it is imperative to study the real-time detection of cyber attacks to facilitate effective supervision and regulation. To this end, this paper proposes the weighted and extended isolation forest algorithms and designs a novel framework for the real-time detection of cyber-attack transactions by thoroughly studying and summarizing real-world examples. Furthermore, this study develops a new detection approach for locating the compromised address of a cyber attack to resolve the data scarcity of hack addresses and reduce time consumption. Moreover, three experiments are carried out not only to apply on different types of cyber attacks but also to compare the proposed approach with the widely used existing methods. The results demonstrate the high efficiency and generality of the proposed approach. Finally, the lower time consumption and robustness of our method were validated through additional experiments. In conclusion, the proposed blockchain-oriented approach in this study can handle real-time detection of cyber attacks and has significant scope for applications.

Introduction

As a new decentralized infrastructure and disruptive core technology, the public blockchain technology has piqued the interest of researchers, Footnote 1 and the number of academic studies on blockchain is growing rapidly (Xu et al. 2019 ). According to Fang et al. ( 2022 ), Ethereum has become the mainstream blockchain platform for public blockchains, accounting for most of the total market capitalization. Currently, Ethereum is the largest decentralized open-source blockchain system that provides Turing-complete programming language to develop smart contracts. In consequence, several decentralized applications ( dapps ) based on smart contracts, such as Uniswap, Footnote 2 Aave, Footnote 3 and PanacakeSwap, Footnote 4 have emerged, and they have been applied to many areas, especially finance, arts, and collectibles. However, with the proliferation of dapps on the public blockchain, the account-based blockchain platforms have become a breeding ground for various financial scams and hacks due to their anonymity and enormous financial values. Cyber attacks and illegal activities are increasing on the account-based blockchain platforms (e.g., Ethereum, Footnote 5 Binance Smart Chain Footnote 6 (BSC), and SOLANA Footnote 7 ). Furthermore, according to the Rekt database, Footnote 8 over $1.9 billion have been lost in 161 attacks on the decentralized application in 2021, indicating that cyber attacks have been a critical issue for the public blockchain. As the anonymity of blockchain provides convenience for hackers, an increasing number of financial regulators are attempting to strengthen blockchain supervision in various countries (Sebastião and Godinho 2021 ). Facing this issue, this paper aims to propose a general and real-time approach to detect cyber attacks to facilitate effective supervision and regulation in the field of the public blockchain.

In particular, focusing on the top 30 cyber attacks (ranked by the funds lost) appearing in recent years, as shown in Table 1 , the account-based blockchain platforms have been targeted by three types of cyber attacks: Smart contract exploits, Flash loan attacks, and Identity theft. We will briefly introduce the three types of cyber attacks (Table 1 ), because they are the targets that the proposed new approach must detect. Smart contract exploit is the most frequent cyber attack in recent years since the decentralized applications are run on an open-source smart contract, which are written in programming languages and solely controlled by its own code. Hence, hackers have the opportunity to review code and probe the networks to look for code vulnerabilities of smart contracts, such as the vulnerabilities of re-entrancy, integer overflow, and multisig (Aspris et al. 2021 ; Efanov and Roschin 2018 ; Harvey et al. 2021 ). Note that the vulnerabilities mainly exist in smart contracts on account-based blockchain; therefore, the compromised addresses of Smart contract exploits belong to smart contracts. A real-world example related to the Smart contract exploit is described in “Appendix 2 ”. Regarding Flash loan attack ; hackers exploit economic vulnerabilities in the interaction between the decentralized applications of flash loans and other smart contracts. This enables hackers to borrow, arbitrage, and liquidate assets in an extremely short period, resulting in illegal profits (Qin et al. 2021 ), the most common method being arbitrage trading, in which the price of a crypto asset is manipulated on one decentralized exchange and quickly resold on another. In general, the compromised address of arbitrage trading based on flash loans is always the address of decentralized applications, which also belongs to smart contract. “Appendix 2 ” also provides detailed information about the arbitrage trading. Aside from the two types mentioned, Identity theft is a common type of cyber attacks (Fang et al. 2022 ; Xu 2016 ). It refers to a scenario in which a hacker gains unauthorized access to an individual or organization’s private key through phishing attacks, malware, or social engineering tactics, allowing them to access the associated blockchain account and transfer funds to the hacker’s account. According to the transaction definition (see “Appendix 1 ”), all transactions need to be signed using corresponding private keys before the transactions are submitted to the account-based blockchain. Therefore, losing authority of private keys is equal to losing funds on blockchain. The externally owned account (EOA), explained in “Appendix 1 ”, is thus a compromised address of Identity theft, since the private key on blockchain controls only an EOA. Based on the introduction of different cyber attacks, the compromised addresses of Smart contract exploit and Flash loan attacks belong to smart contracts, whereas those of Identity theft belong to EOAs. Meanwhile, both Smart contract exploit and Flash loan attack refer to exploiting code and economic vulnerabilities, respectively. In fact, the various types of cyber attacks imply that the key clues that must be detected differ. Accordingly, the first motivation for our work is to propose a general approach framework capable of dealing with multiple types of cyber attacks rather than just one or two types, especially since we believe that new types will emerge in the near future.

Although supervised machine learning methods ( SML ) have succeeded in numerous fields, they also suffer from several challenges when dealing with this paper’s problem of detecting cyber-attack transactions in blockchain. The first difficulty stems from insufficient data. The adopted SML is part of the postmortem analysis technology, which means that the existing public transaction information is required to carry out the identity inference of the illegal addresses, such as the behavior analysis of the addresses and account identification. However, a dynamic and ever-changing cyber attack is hard to keep up. Only the historical types rather than the latest ones of cyber attacks can be learned, because not all labels can be available immediately (Carcillo et al. 2018 ; Dal Pozzolo et al. 2014 ). The second difficulty stems from the data’s imbalance . In fact, the annotated information of blockchain addresses published on third-party sites is relatively scarce, resulting in data imbalance. As a consequence of the data imbalance issue, SML will be biased toward the majority class, resulting in poor classification performance of minority classes, because only the majority class of cyber attacks can be fully learned (Thabtah et al. 2020 ). The third challenge lies in time consumption . Real-time detection of cyber attacks is crucial for victims and managers to take corresponding measures to prevent potential losses. According to Etherscan 8 and Bscscan, Footnote 9 the average block time on the BSC and Ethereum is approximately 2.5 and 12 s, respectively. However, SML consumes far more time than average block times (Chen and Guestrin 2016 ). Therefore, completing the analysis of the real-time transactions within a short period is one of the most immense challenges for detecting cyber-attack transactions. Facing these challenges that SML is hard to cope with, the second motivation for this paper is to develop a new real-time method (i.e., with very low time consumption) that does not require adequate and balanced data.

Compared with the aforementioned SML, unsupervised machine learning ( UML ) methods can compensate for the deficiency of the aforementioned SML to some extent. Many existing studies demonstrate this point: (i) UML has been applied for credit card fraud detection by dealing with fraudsters’ ability to invent novel fraud behaviors and changes in customer behaviors (Carcillo et al. 2021 ); (ii) for telecommunications fraud detection by correcting the misclassification of behavior types and recognizing the dynamic appearance of new fraud types (Hilas and Mastorocostas 2008 ); and (iii) for bot recognition in a web store by identifying more camouflaged agents (Rovetta et al. 2020 ), among others. Accordingly, UML can handle many types of cyber attacks, whether it has known them before or not, because many examples have illustrated that UML can discover patterns and information that may seem strange or suspicious. Fortunately, as a typical UML, the isolation forest (Liu et al. 2012 ) has been demonstrated to be an effective method for anomaly detection with low time consumption and high efficiency in several fields, such as biostatistics and semiconductor manufacturing (Liu et al. 2012 ; Puggini and McLoone 2018 ), where the detection of cyber attacks falls under the category of anomaly detection. Furthermore, recent years’ work has improved the traditional isolation forest into an extended isolation forest (EIF) by adjusting the way branch cuts are made (Hariri et al. 2019 ). However, in our experiment, the EIF also performs poorly. The third motivation of this paper is to develop a classic isolation forest and EIF for achieving satisfactory results in detecting cyber attacks on the account-based blockchain.

Facing the three listed motivations, the main work is introduced as follows. First , we propose a novel method for extracting real-time account-based blockchain transactions from open-source websites, such as Etherscan and Bscscan, and identifying the addresses with the highest expenditure as the target addresses based on the accumulated expenditures of various crypto assets. Because most target addresses have a long usage history, the data deficiency of the hack addresses can be solved in this manner, responding to the first motivation. Second , the target addresses will be filtered by the funds expenditure threshold, and only a few data points will be fed into the next stage of the detection system, resulting in a reduction in time consumption, which responds to the second motivation. Third , we extract historical transaction data of the target addresses using open-source websites and use various data preprocessing methods to process the original data feature, allowing more useful information to be extracted. Then, in response to the third motivation, we propose an improved algorithm that assigns an anomaly score to the depth of the isolation tree based on the traditional EIF, dubbed weighted and extended isolation forest ( WEIF ).

To summarize, the main contributions are listed. The first contribution is that this work is one of the first to conduct an in-depth study into the real-time detection of cyber attacks on account-based blockchains. This work not only considers various types of cyber-attack transactions by developing real-world cyber-attack examples, but it also develops a new general UML-based framework with low data request and computation costs. The second contribution is to propose an effective strategy for identifying suspected compromised addresses and filtering them using a fund expenditure threshold, which will significantly reduce the number of analyzed targets. The third contribution is the designed dynamic modeling technology, which refers to the development of an evolving model for mining behavioral differences between suspected compromised targets. As a result, the designed dynamic model can detect real-time and constantly changing cyber-attack transactions. Last but not least, by adding weight to the depth of EIF, a new algorithm called WEIF is created. When the weight is introduced, the gap between the average depth of the normal transaction and the average depth of the cyber-attack transaction grows larger than in the famous EIF. In fact, the larger the gap, the easier it will be to distinguish cyber-attack transactions. The results of three types of cyber attacks show its high efficiency and generality.

The remainder of this paper is organized as follows to present our work and contributions logically. “ Related work ” section examines SML and UML related works, and the detailed information on the traditional isolation forest and its extension. “ Methodology ” section presents the overall framework for detecting cyber attacks, as well as our proposed algorithm and its validation results based on simulation data. “ Experimental evaluation ” section shows the detailed information about the training dataset and the analysis of experimental results. Finally, “ Conclusion and future work ” section concludes and discusses future work.

Related work

In essence, detecting cyber-attack transactions can be considered as identifying rare transactions that deviate significantly from most of the transactions. Because blockchain’s openness makes it easier for researchers to access transaction data, an increasing number of researchers are working on developing new technologies to detect various types of cyber attacks on the blockchain. The majority of related studies focused on the use of SML, with only a few studies focusing on UML. Therefore, we introduce related studies of SML and UML. As aforementioned, our proposed algorithm is developed based on the traditional isolation forest and its extension. Thus, the theories of the standard isolation forest and its extension are also elaborated in this section. These mentioned methods reviewed in this section will be compared with our proposed algorithm in “ Experimental evaluation ” section.

SML is a type of machine learning in which an algorithm is trained on a labeled dataset to recognize patterns and make predictions. The labeled dataset for SML consists of input data and corresponding output labels. SML evaluates its accuracy using the loss function and learns from training data until the error is decreased sufficiently. As an increasing number of cyber attacks of blockchain are provided on open-source websites, such as Etherscan, Bscscan, and Rekt database, most researchers attempt to collect malicious addresses via these open-source websites and focus on the use of SML to detect malicious transactions by learning the transaction behaviors of malicious addresses.

According to Farrugia et al. ( 2020 ), the XGBoost classifier was used on a balanced dataset (4,699 accounts) to detect malicious accounts on the Ethereum blockchain. Despite its 96% accuracy, the execution time of this model is more than 62 s, which is much longer than the average block times of Ethereum and BSC. Accordingly, it indicates that the XGBoost classifier is incapable of detecting cyber-attack transactions on account-based blockchains in real time. According to Aziz et al. ( 2022 ), various SML methods, including random forest, XGBoost, and the light gradient boosting machine (LGBM), have recently been used to detect fraud transactions by learning the transaction behavior of labeled accounts, and all of these models achieve more than 93% on the F1 score. All three mentioned models belong to ensemble learning techniques, which is introduced in Table 2 .

Furthermore, some researchers have applied graph convolutional network ( GCN ) techniques (Shen et al. 2021 ; Yu et al. 2021 ) to the identity inference of phishing scams and Ponzi scheme based on the balanced dataset, and these techniques have also achieved good performance with around 90% on F1 score. A brief description of GCN is also provided in Table 2 .

According to the existing SML-related studies, SML methods are effective for the identification of malicious addresses based on balanced datasets. However, the datasets for anomaly detection of cyber attacks on account-based blockchain are extremely imbalanced, which may result in poor performance and the generality of SML methods. In recent years, various types of UML have been used to deal with the imbalanced datasets, and the detailed information of UML is introduced as follows.

As mentioned above, the methods of SML are much more resource intensive because of the need for labeled data, but the methods of UML discover the hidden patterns or the data cluster without the human intervention. Currently, UML is the primary technology for detecting anomalies in many fields, including finance, telecommunications, and network administration (Carcillo et al. 2021 ; Hilas and Mastorocostas 2008 ; Rovetta et al. 2020 ). However, UML methods for detecting cyber attacks on the blockchain have received little attention. Only kernel-based techniques are used to detect abnormal addresses in the historical transaction network (Patel et al. 2020 ), and the kernel-based technique achieves a F1 score of approximately 80%. This section introduces the techniques of distance-based, clustering-based, histogram-based, kernel-based, neural network-based, and ensemble-based to fully understand how UML techniques work and apply these techniques to the detection of cyber-attack transactions on account-based blockchains, as shown in Table 3 .

Among the UML methods shown in Table 3 , the existing experiments on public datasets have shown that the isolation forest algorithm outperforms the other common UML techniques in terms of efficiency and accuracy while consuming significantly less memory (Falcão et al. 2019 ; Liu et al. 2012 ). However, the random isolation forest cuts are always horizontal or vertical, resulting in bias and artifacts in the anomaly score map (Hariri et al. 2019 ). Hariri et al. ( 2019 ) proposed the EIF to mitigate bias by using a random slope and a random intercept for branch cuts. Therefore, we use the traditional isolation forest and EIF as the base of our proposed model in this paper, and the detailed information on the isolation forest and EIF is further introduced as follows.

Isolation forest and its extensions

Isolation forest

An isolation forest, like a random forest, is built with decision trees, which belongs to UML methods because there are no predefined labels. The central idea behind isolation forest is to isolate anomalies by constructing a series of isolation trees with random attributes (Liu et al. 2012 ). The isolation tree is constructed using the algorithm shown in Table 12 (“Appendix 3 ”) by splitting the subsample observations over a split value of a randomly selected attribute. In this manner, observations with corresponding attribute values less than the split value go left, whereas others go right, and the process is repeated recursively until the tree is fully constructed. The split value is randomly selected between the selected attribute’s minimum and maximum values. Although the isolation forest is a typical method of UML, its random cuts are always straight lines, making the random cuts to be either horizontal or vertical. Therefore, several extensions of the isolation forest have been developed in recent years (Hariri et al. 2019 ).

Among the isolation forest algorithms developed, the EIF performs better (Hariri et al. 2019 ), which eliminates the disadvantage of isolation forest by adjusting the way of branch cuts. In contrast to the isolation forest, the EIF determines the information of random slope and intercept for the branch cut on a multidimensional dataset. The methods for generating the random slope and intercept for the branch cut will be briefly introduced here. In terms of the random slope for the EIF, it is a normal vector denoted as \({ }\user2{n }\) by drawing a random number for each coordinate of \({\varvec{n}}\) from the standard normal distribution N (0,1). As a result, the branch cut is a hyperplane for the high-dimensional dataset rather than a straight line. In terms of the intercept denoted as \({\varvec{p}}\) , it is chosen from the value range of the training data. For a given point \({\varvec{x}}\) , the branching criteria for the data splitting are shown as follows:

if the condition is not satisfied, the data point \({\varvec{x}}\) moves down to the right branch, otherwise it will be passed to the left branch. By this way, the value of intercept will be restricted to available data at each branch point when we construct trees with larger depths. The criteria for choosing intercept results in more possible branching options for areas of data concentration and less possible branching for areas of fewer observations.

Except for the construction of isolation trees, there are few differences between the isolation forest algorithm and the architecture of EIF, which includes four procedures: isolation tree construction, depth computation, EIF construction, and anomaly score computation (Hariri et al. 2019 ). First, the isolation trees for the EIF are built using the Eq. ( 1 ) as described in Table 13 (“Appendix 3 ”). Secondly, how to compute the depth of an observation on a given extended isolation tree is elaborated in Table 14 (“Appendix 3 ”). Finally, Table 15 (“Appendix 3 ”) shows the construction of an EIF and the computation of anomaly scores based on the isolation tree and depth computation.

Methodology

First of all, we propose a general framework for detecting cyber attacks in the context of the account-based blockchain. As shown in Fig.  1 , this framework comprises four stages: the data source, data clean, feature process, model training and prediction. In the stage of data source , hundreds of real-time transactions in a block, referring to about 200 transactions every 12 s on Ethereum or about 150 transactions every 3 s on BSC, are extracted through open-source websites. For the stage of data clean , it is separated into the identification of compromised addresses, data extraction of historical transactions, and feature generation based on transaction behaviors, which will be executed in sequence. When it comes to the stage of the feature process , all the continuous features will be processed by data normalization and three sigma processes, and then all of the discrete features and the processed continuous features will be merged and fed into correlation analysis. During the final stage of model training and prediction , all the training data will be trained on our proposed new algorithm, WEIF, one of our main contributions in this work. The detailed information of each procedure in our proposed framework will be stated in the following subsections. Meanwhile, the evaluation metric is also introduced.

The framework of anomaly detection proposed in this work

Data source

For the data source stage, all transactions are obtained from Etherscan and Bscscan, which are the leading platforms for Ethereum and BSC, respectively. A complex transaction on Etherscan is used as an example to demonstrate all of the detailed transaction information used in this paper (Fig.  2 ). The entire transaction details are divided into six parts, as shown in Fig.  2 . In detail, the block number in Part 1 is the location where transactions are stored and encrypted, and it is generated every 2.5 s on the BSC and every 12 s on Ethereum, respectively. This section contains all of the smart contract function calls for Part 2. Part 3 includes the transaction initiator, which refers to the EOA mentioned in “Appendix 1 ”. For Part 4, all the transfers of the native crypto assets, referring to the ETH and BNB, are shown in this Part. For Part 5, all the token transfers, known as the nonnative crypto assets transfers, are contained. Apart from the parts mentioned above, the transaction value and transaction fee are included in Part 6, where the transaction fee is equal to the product of gas price and quantity of gas consumed in a transaction.

The transaction details of account-based blockchains as an example

According to the definition of transaction on the blockchain, the transaction details can also be divided into four categories (i.e., external transaction, internal transfer, token transfer and transaction action). With regard to the external transaction, the transaction details of Part 1, Part 3, and Part 6 in Fig.  2 make up the basic information of external transactions. Regarding the internal transfer, the information in Part 4 contains all the internal transfer information. With regards to the token transfer, Part 5 represents the token transfer of the transaction. Apart from the three categories introduced above, Part 2 is named as the transaction action for transaction on the blockchain.

In particular, not all the transactions contain all six parts of the transaction details in Fig.  2 . In fact, the more complicated transactions contains more parts of transaction details on the account-based blockchain. For instance, the cyber-attack transactions of Identity theft are mainly composed of external transactions and token transfers, while cyber-attack transactions of Flash loan attack and Smart contract exploit usually involve all parts in Fig.  2 .

Three different procedures will be executed in sequence during the data clean stage, which are the identification of compromised addresses, data extraction, and feature generation.

Identification of compromised address

The compromised address is the target of hackers owing to a large quantity of crypto assets in the compromised address. Meanwhile, the majority of the compromised addresses exhibit long-term usage behavior. However, because the majority of hacker addresses are newly created for the cyber attacks, only a few transaction records are available for behavior analysis. As a result of more historical transactions than the hacker address, the behavior analysis of the compromised address for the cyber attack is easier to conduct.

As a result, a specific strategy is proposed in this paper to find the address with the highest spend volume as the suspected compromised address, despite the fact that the compromised addresses for real-time transactions are unknown. Based on the transaction details shown in Fig.  2 , the suspected compromised addresses are located by computing the cumulative total expenditure of various crypto assets. The data deficiency of hacker addresses can thus be addressed to some extent.

Furthermore, over one million transactions are generated every day on the blockchain according to the statistics of Etherscan. In consequence, analyzing all the transactions in real-time is not feasible. To make our proposed framework to be suitable for the real-time analysis, we propose a novel approach by filtering the transactions according to the expenditures of the suspected compromised addresses. Obviously, with this transaction filtering approach, not too many transactions will be fed into the following analysis of the proposed framework. Thus, the time cost of the proposed framework can be significantly reduced in this way.

Data extraction

In fact, the real-time detection of cyber attacks only depends on the behavior of recent transactions due to the ever-changing behavior of transactions. Meanwhile, the suspected compromised address’s numerous transactions make data extraction time consuming. Consequently, as shown in Fig.  3 , we set a limit of transaction records, referring to the dynamic window, to construct the dataset of machine learning methods. For the compromised address of cyber-attack transaction, the window size is set as s , and \(T_{k}\) represents the cyber-attack transaction marked as red, where k is the transaction number of compromised address. Then, if the number of historical transactions before the cyber-attack transaction is larger than s , the transactions \([T_{k - s} , T_{k - s + 1} , \ldots , T_{k} ]\) , marked as blue, will be extracted. Otherwise, the window size is equal to the number of historical transactions before the cyber attack and the transactions \([T_{1} , T_{2} , \ldots , T_{k} ]\) will be extracted. This way, the time cost can be further reduced by the data extraction method. In particular, the different window sizes will be chosen to verify the stability and efficiency of our proposed algorithm.

Using dynamic window to construct the dataset from the historical transactions of compromised address. The cyber-attack transaction is marked as red, and the transactions marked with blue are recent transactions within the window size

Feature generation

According to the introduction of the transaction details, we establish a general feature set for all types of cyber attacks based on the transaction details of external transactions, internal transfer, token transfer, and transaction action. Therefore, all the features are separated into four categories (Table 4 ). Regarding the external transaction features, seven features are generated from the basic information of the external transactions, in which an EOA sends native crypto assets directly to another EOA or smart contract (see “Appendix 1 ”). Regarding the internal transfer features, these features, referring to transfer volume and transfer count, are computed from the internal transfer carried out through a smart contract as an intermediary. The token transfer features are extracted from the transactions with token transfers that refer to the transfers of ERC-20 Footnote 10 or BEP-20 Footnote 11 tokens in the transaction details. Apart from the three categories of features mentioned above, the transaction action features are generated based on the specific function call of the smart contract, as shown in the transaction action in Fig.  2 .

Feature process

We process feature data using three methods (data normalization, three sigma process, and correlation matrix) to extract more useful information and speed up model training for anomaly detection.

Data normalization

Variables measured at different scales do not contribute equally to model fitting and may result in bias. To address this potential problem, Standard Scaler, also known as z score, is used for data normalization to speed up the model training and improve the model performance (Ioffe and Szegedy 2015 ). All feature values are rescaled to the new distribution so that the mean of observed values is 0 and the standard deviation is 1. The specification is expressed as

where \(\mu\) is the mean and \(\sigma\) is the standard deviation of the original data.

Three sigma process

In fact, the probability of occurrence decreases as the value deviates from the mean. Consequently, we apply the probabilistic rules of normal distribution to process transaction features. According to the normal distribution, the standard deviation, that is, sigma ( σ ), defines how far the normal distribution is spread around the mean. For an approximately normal distributed dataset, it follows a set of probabilistic rules described as follows: 68% of all values fall in [mean  − σ , mean +  σ ], 95% of all values fall in [mean − 2 σ , mean + 2 σ ], and 99.7% of all values fall in [mean  −  3 σ , mean + 3 σ ]. According to the rules, there are only 0.3% values falling outside three times the sigma range (3 σ ), and thus we can judge these values that fall outside [mean − 3 σ , mean + 3 σ ] to be anomalous.

Correlation analysis

In fact, some features, while highly relevant to the specific type of cyber attacks, may be redundant. Meanwhile, if two independent features are highly correlated, they are considered redundant. Therefore, although eliminating redundant variables may not result in a significant loss of accuracy, it does result in a very efficient model under many constraints. In our proposed framework, the correlation analysis is used for feature selection by removing redundant features. The correlation coefficient of correlation analysis, denoted r , ranges from − 1 to + 1 and quantifies the direction and strength of the linear association between two features. Furthermore, the correlation coefficient is denoted as

where \(n\) is the size of feature data, \(p_{i}\) and \(q_{i}\) are the individual features index with i , \(\overline{p}\) and \(\overline{q}\) are the mean value of two individual features.

Proposed algorithm

According to the definition of EIF, the random slope and intercept for branch cuts should be determined before each branch cut during EIF construction, with a lower average depth indicating a more abnormal observation. The normal observation on a few trees may be close to the root due to the random selection of slope and intercept, whereas the abnormal observation on a few trees may be far away from the root based on EIF. As a result, an observation’s anomaly score, calculated based on the average depth of the extended isolation trees, may deviate from its true depth range on the isolation tree, resulting in a bias. To mitigate the bias, we propose a novel algorithm, named as WEIF , by weighting the original depths of given observations in EIF, where the anomaly score of given observation is computed based on the average of weighted depths processed by the algorithm in Table 5 .

In general, when the random trees of a forest produce shorter path lengths for some specific points, they are highly likely to be anomalies. Depths less than the first quartile of the original depths will be increased if the median of the original depths is greater than its mean, according to our proposed algorithm in Table 5 . Depths greater than the third quartile of the original depths, on the other hand, will be decreased if the median of the original depths is less than its mean. Furthermore, if the median of the original depths is equal to its mean, the depths will not change. By this way, the depth difference between the normal observation and abnormal observations is becoming larger. The complexity of our proposed algorithm for training and prediction are \(O\left( {t\psi log\left( \psi \right)} \right)\) and \(O\left( {ntlog\left( \psi \right)} \right)\) , respectively, where \(t\) is the number of trees, \(\psi\) is the subsample size of data and \(n\) is the number of observations in the dataset.

According to EIF, the architecture of WEIF contains four steps (Fig.  4 ): generating sub-datasets, constructing isolation trees, weighting the depths, and computing anomaly score. Compared with the architecture of the traditional isolation forest and its extension, there are few changes except for weighting the depths based on the algorithm shown in Table 5 . Specifically, the definition of the anomaly score for an observation y is described as

where \(E\left( {h\left( y \right)} \right)\) is the mean value of weighted depths for a given observation in all trees, \(c\left( n \right)\) is used to normalize the average path length \(E\left( {h\left( y \right)} \right)\) that is defined as the average path length of unsuccessful search in Binary Search Tree (Liu et al. 2012 ), i.e.,

where \(H\left( i \right)\) can be calculated by \(ln\left( i \right)\)  + 0.5772156649 (Euler’s constant) and n is the number of observations in a given dataset (Liu et al. 2012 ).

Architecture of weighted and extended isolation forest. Firstly, several sub-datasets are generated by randomly sampling from the training dataset. Secondly, all the sub-datasets are passed into the construction of the isolation trees, and the original depths of the observations in the sub-datasets are computed for each isolation tree. Thirdly, all the original depths are weighted based on the algorithm in Table 5 . Finally, the anomaly score is calculated on the average depth of the weighted depths

Recalling Eq. ( 4 ), a smaller \(E\left( {h\left( y \right)} \right)\) means a higher anomaly score. By this way, all of the observations will be passed into the isolation trees and assigned an anomaly score. And the observation with a higher anomaly score is more anomalous based on Eq. ( 4 ). Specially, the threshold of the anomaly score shown in Fig.  4 is decided by the expected proportion of anomalies in the whole dataset, named as contamination in this paper.

Illustrative examples

To understand how WEIF works, we provide two illustrative examples of a two-dimensional dataset sampling from two-dimensional distribution with zero mean vector and an identity covariance matrix. The first example focuses on the comparison of normal and abnormal observations processed by our proposed algorithm, while the second example demonstrates the differences in the outputs of our proposed algorithm (WEIF) and EIF.

For the first example, the depth comparison of the normal and abnormal observations are presented with different forms. First, all the observations of the two-dimensional dataset are plotted in the scatter plot in Fig.  5 a, where few samples exist further away from the center of the two-dimensional dataset. Second, a contour plot in Fig.  5 b is achieved based on the average depths of all the observations computed by our proposed algorithm. Here, the color of the observation far away from the data center is darker than the observation close to the data center, indicating that the abnormal observation can be effectively identified by our proposed algorithm. Furthermore, the results of comparing the depths of the normal and abnormal observations with the depths computed by our proposed algorithm are shown in Fig.  6 . As shown in Fig.  6 a, the abnormal observation is quickly isolated, whereas the normal observation continues all the way to the bottom of the tree. All of the depths of the observations on the isolation trees are shown as straight lines in Fig.  6 b, and it is obvious that the majority of the depths of the abnormal observations are shorter than the normal observations.

Scatter plot and anomaly score map in this example. a The data of scatter plot are sampling from two-dimensional distribution with zero mean vector and identity covariance matrix. b The anomaly score map is plotted based on the weight depths processed by WEIF. A darker color means to be more anomalous

Structure of a single Tree and depths of WEIF. The results of normal observation are marked with blue, while the results of abnormal observation are marked with red. a The paths for a normal observation and an abnormal observation are plotted in a single isolation tree. b The depths of an observation in the whole forest are displayed as the radial line and the length of line represents the value of depth

For the second example, the depths of a normal and abnormal observations are computed by WEIF and EIF, respectively. For comparing the results of WEIF and EIF, the depths of a normal observation for WEIF and EIF are plotted in Fig.  7 a, while the depths of an abnormal for WEIF and EIF are plotted in Fig.  7 b. The blue and red lines represent the depth of each tree processed by EIF and WEIF, respectively. Compared with EIF, the depths of a normal observation are increased by our proposed algorithm, as shown in Fig.  7 a, whereas our proposed algorithm decreases the depths of an abnormal observation in Fig.  7 b. In consequence, the difference in the average depth for the normal and abnormal observations is becoming larger for our proposed algorithm in contrast to EIF.

Comparison of the depths processed by WEIF and EIF. The red and blue lines represent the depths computed by WEIF and EIF, respectively

Evaluation metric

The F1 score is chosen to be the main metric of the models, which is the Harmonic Mean between precision and recall. The range for the F1 score is [0, 1]. It tells us how precise our classifier is and how robust it is. The greater F1 score indicates the better performance of our model. The precision, recall, and F1 score are formulated as follows:

True positive represents the number of real cyber attacks correctly detected, false positive represents the number of normal transactions wrongly detected as the cyber attacks, and false negative represents the number of real cyber attacks detected as the normal transactions.

Experimental evaluation

Several experiments on various types of cyber attacks are carried out to assess the efficiency and robustness of our proposed algorithm against all of the methods mentioned in “ Related work ” section.

Dataset and experimental setup

All the detailed information on cyber-attack transactions is extracted from the open-source websites, referring to Etherscan, Bscscan, and Rekt Dataset. First, all the labeled cyber-attack transactions are extracted from the Rekt Dataset and classified into Smart contract exploit, Flash loan attack, and Identity theft. Second, 66, 62, and 58 compromised addresses of cyber-attack transactions are extracted for the Smart contract exploit, Flash loan attack, and the Identity theft based on the identification strategy of compromised address. Specially, 200 addresses that hackers have not attacked are also randomly extracted from Etherscan and Bscscan for verifying the effectiveness of the model, which are made up of smart contracts and EOAs and are similar to the compromised addresses of all cyber-attack transactions. Finally, the historical transactions of each compromised address are extracted from Etherscan and Bscscan using the data extraction strategy shown in Fig.  3 , with the window size set to 2000.

To display the distribution of datasets for the compromised addresses and the differences between the three types of cyber attacks, this study computes the descriptive statistics of transaction quantity for the compromised addresses, as shown in Table 6 . The descriptive statistics in Table 6 yield three findings. First, more than 50% compromised addresses of Identity theft have more than 2000 historical transactions, whereas more than 75% compromised addresses of other cyber attacks have less than 2000 historical transactions. The statistical results indicate that most of the Flash loan attacks and Smart contract exploits have been launched at the beginning of the decentralized applications, and that the compromised addresses of Identity theft have always been used for a long time or high-frequency trading. Second, the number of historical transactions on Identity theft, Smart contract exploit, and Flash loan attacks gradually decreases based on the values of median, third quartile, and max (see Table 6 ), demonstrating that the three types of cyber attacks are different from each other. Finally, as all values of the first quartile for all types of transactions are larger than 100, this indicates that the data deficiency of hack addresses is solved by extracting the historical transactions of the compromised addresses.

After the data extraction, several features (mentioned in “ Methodology ” section) are generated from external transactions, internal transfers, token transfers, transaction actions. To take full advantage of the transaction features, the descriptive statistics and correlation analyzes are carried out in this paper, whose result are shown in Table 7 and Fig.  8 . As shown in Table 7 , most of the min values of the features are equal to 0, since most of the normal transactions are simple transactions compared to the cyber-attack transactions.

Results of correlation analysis

According to the result of the correlation analysis in Fig.  8 , the type of cyber attack is heavily related to the external features and token transfer features (i.e., gas fee, spend volume, spend-anomaly, to–ts-count and to-ts–count-anomaly). Meanwhile, the redundant features exist in the feature data as a result of the correlation analysis. For example, the correlation coefficients between internal transfer features (i.e., inter-tx-volume, inter-ts-volume-50%, and inter-ts-volume-max) are extremely high, resulting in a lower model efficiency. To improve the detection model’s efficiency, we remove redundant features prior to model training.

Experimental setup

To provide sound results, the dataset of the compromised address is split into a train dataset and a test dataset according to the ratios of 70% and 30%. Furthermore, the expected proportion of anomalies in the entire dataset is set at 5% for all UML methods, as mentioned in “ Methodology ” section. For our proposed algorithm, many parameters should be decided before model training, such as the multiplier in Table 5 , extension level, and the number of isolation trees in Table 15 (“Appendix 3 ”). The grid search technique is used to estimate the parameters of WEIF in order to find the optimal parameters, as it has always been used to find the optimal parameters for several algorithms such as SVM and neural network algorithm (Pontes et al. 2016 ; Syarif et al. 2016 ). Based on parameter estimation using sklearn’s GridSearchCV, Footnote 12 the multiplier, the extension level, and the number of isolation trees of WEIF are equal to 2, 4, and 100, respectively.

Based on the experimental setup, this section aims to provide two types of results: one is about the efficiency and generality of our proposed algorithm (WEIF) and the other is about the importance of the generated features in detecting cyber-attack transactions on blockchain.

The performance of WEIF

Our proposed algorithm, as well as the comparative algorithms mentioned in “ Related work ” section, are used in three different types of cyber attacks. The results of the Smart contract exploit, the Flash loan attack, and the Identity theft are shown in Tables 8 , 9 and 10 , where all the recalls of GCN are equal to zero because of the heavily data imbalance and hence, its result is not included. According to the experimental results, we have the following finds.

According to the results of the Smart contract exploit in Table 8 , WEIF has the highest F1 score on the test dataset. In Table 8 , the difference in F1 score between the two results of datasets is small, and the average F1 score of UML is around 0.83 for the test dataset. In Table 8 , all SML methods perform well on the train dataset when compared to UML methods, but the average F1 score of SML on the test dataset is less than 0.5. As for the result of the Flash loan attack in Table 9 , WEIF outperforms all comparative models with 0.906 on the F1 score for the test dataset, and the average F1 score of UML is about 0.87 on the test dataset. However, although the methods of SML have a good performance on the train dataset, these methods work poorly on the test dataset, as shown in Table 9 . As for the results of Identity theft in Table 10 , WEIF achieved the best performance with 0.753 on F1 score. Most of the methods belonging to UML have a poor performance on cyber-attack detection of Identity theft except for the Avg k-nearest neighbors (KNN), KNN, EIF, and WEIF, and its average differences in F1 score for both types of datasets are about 0.05. Be similar to the results of SML in Tables 8 and 9 , the SML has a good performance on the train dataset, but poor performance on the test dataset, as shown in Table 10 .

To summarize, three types of cyber attacks can be detected based on the machine learning methods. First, for all three types of cyber attacks, the proposed algorithm achieves the highest F1 score on the test datasets, demonstrating the efficiency and generality of WEIF on different types of cyber attacks. Second, all of the F1 scores of SML on train datasets are significantly higher than those on test datasets, whereas the differences in F1 scores of UML for the two types of data sets are negligible. As a result, SML performs less effectively than UML in detecting cyber attacks on account-based blockchains. Last but not least, when compared to EIF, our proposed WEIF achieves F1 score improvements of about 0.03, 0.04 and 0.05, respectively, indicating that computing the anomaly score based on the weighted depths of the EIF makes our proposed model more efficient.

Feature importance

Generally speaking, a higher feature importance score means that the specific feature will have a larger effect on the model. In order to determine out which feature is significant for the cyber-attack detection, random forest is applied in the analysis of feature importance owning to its best performance of cyber-attack classification among all methods of SML, as shown in Tables 8 , 9 and 10 . Figures  9 , 10 and 11 show the results of feature importance for three types of cyber attacks. As shown in Figs. 9 , 10 and 11 , there are some differences and similarities in the analysis of feature importance for three types of cyber attacks.

Feature importance of Smart contract exploit

Feature importance of Flash loan attack

Feature importance of Identity theft

In terms of the differences between the three types of cyber attacks, the number of significant features differs based on the results of feature importance. In particular, the top five features account for more than 70% of the impact in detecting Smart contract exploits, as shown in Fig.  9 , whereas the cumulative importance score of the top ten features is about 60% for the detection of Flash loan attack in Fig.  10 and the top three features account for about 90% of impact for the detection of Identity theft in Fig.  11 . In terms of similarity between three types of cyber attacks, some features are shared by all types of cyber attacks, as illustrated in Figs. 9 , 10 and 11 . For example, the feature of gas fee is included in the top ten significant features for all types of cyber attacks, demonstrating that more complicated operations within the cyber-attack transaction result in more gas consumed when compared to normal transactions. To sum up, although the cyber-attack transactions are different, the features designed in this paper are general for all types of cyber-attack detection based on the high F1 scores achieved by WEIF, shown in Tables 8 , 9 and 10 . Furthermore, the significant features for the three types of cyber attacks are almost the same as the results of feature importance for LGBM, as shown in Figs. 16 , 17 , 18 (see “Appendix 4 ”). This demonstrates that the result of feature importance does not significantly depend on the selection of SML methods.

Additional validations

Two additional validations are carried out to evaluate our proposed algorithm’s efficiency and robustness. The first validation is to conduct a robustness test of WEIF based on a new dataset. The second validation is to conduct experiments on different window sizes of training data, mentioned in the data extraction, to determine whether our proposed approach is robust and suitable for the real-time detection of cyber-attack transactions.

The first validation is performed on the new dataset, which contains all of the compromised addresses from cyber attacks as well as an alternate set of 100 random addresses that were not attacked by hackers. The robustness tests on WEIF for all three types of cyber attacks follow the same process as the previous section, and the results are listed in Table 11 . Compared to the results shown in Tables 8 , 9 and 10 , the performances for all three types do not decrease, which indicates the robustness of WEIF.

Six experiments are designed in the second validation to test the robustness and execution time of WEIF by using different sizes of training data mentioned in “ Methodology ” section, i.e., different window size of historical transactions of the compromised address for model training. Figures  12 and 13 show both results. As shown in Fig.  12 , although the training data window size decreases from 2000 to 300, the F1 score of WEIF decreases by only 0.05, with the lowest F1 score being 0.815, indicating the robustness of WEIF again that is not sensitive to the size of the training data . According to Fig.  13 , the average execution time of EIF and WEIF, referring to the average time consumption of model training and prediction for a single transaction, are almost the same, and the gap in the average execution time between WEIF and EIF is also narrowing as the size decrease. This finding implies that, when compared to EIF, WEIF does not significantly increase time consumption. Furthermore, WEIF’s lowest execution time is about 0.6 s, which is significantly less than the average block time mentioned above. As a result of the stability and robustness of our proposed algorithm, the window size can be set to a lower value for the blockchain with lower block time. Otherwise, it can be increased for the blockchain with longer block time.

Results of F1 score with different window sizes of training data

Results of execution time on different window sizes of training data. The blue and red bars represent the execution time of EIF and WEIF, respectively

Based on the results of the experiments above, our study provides important theoretical values and high practical application values for the researchers and industry practitioners, respectively. For the theoretical values , we propose a general framework for cyber-attack detection that incorporates the compromised address recognizer, real-time transaction filter system, general feature generator, and detection model. Our proposed framework addresses the issue of data imbalance and data scarcity of hackers’ addresses. Furthermore, within our proposed framework of cyber-attack detection, we propose a novel algorithm named WEIF that is based on the isolation forest and its extension as the detection model and outperforms all methods on three types of cyber attacks based on experimental results. Based on its strong performance against various types of cyber attacks, our proposed framework and algorithm can serve as a theoretical foundation for improving the supervision and regulation of public blockchains. For the practical application values , the generality, robustness, and low time consumption of our proposed algorithm on different types of cyber attacks have been proved by the experimental results above. First, our proposed algorithm’s generality and robustness to various types of cyber attacks makes it perfectly capable of detecting dynamic and ever-changing cyber attacks on account-based blockchains. Second, because of its low time consumption, our proposed algorithm can detect all real-time transactions in a short period of time. Meanwhile, to reduce the number of real-time transactions analyzed by our proposed algorithm, a novel approach of filtering real-time transactions based on the expenditures of compromised addresses is proposed. Finally, the technology of multiprocesses or multithreads can be used to accelerate the process of detecting cyber attacks. All of these evidences show that our proposed algorithm can be directly applied to the detection of cyber attacks in the blockchain industry in real time.

Conclusion and future work

The dynamic and ever-changing cyber attacks frequently happen on account-based blockchain in recent years. However, only a few technologies of machine learning have been applied for the real-time detection of cyber attacks. To this end, we propose a systematic and comprehensive anomaly detection method for coping with this problem. First, a novel algorithm namely, WEIF, is developed for anomaly detection based on the standard isolation forest and its extended model. Then, we propose a general framework on the basis of our proposed algorithm through a comprehensive study of real-world examples of cyber attacks. Within this general framework, a novel approach of identifying the compromised address is created to solve the hack addresses’ data deficiency and reduce the time consumption of our proposed framework. Next, several experiments are carried out on different types of cyber attacks to verify our proposed algorithm’s efficiency and generality. As expected, the experimental results demonstrate the advantage of our proposed method in contrast to many widely used state-of-the-art techniques. Besides, the result also indicates that the techniques of SML are not suitable for real-time detection of cyber attacks, owing to data imbalance and data deficiency. Finally, the results of additional experiments provide more evidence for supporting the lower time consumption and the robustness of our proposed approach, illustrating that our proposed approach is capable of real-time detection of cyber attacks on the account-based blockchain.

In the future, we plan to extend our work from three aspects. First, we will try to apply the multivariate time-series analysis algorithms to the anomaly detections in the context of the account-based blockchain, since the historical transactions belong to the dataset with a time-series format. Second, crypto exchanges are the main gateway for connecting real-world user information to pseudonymous addresses on account-based blockchain, but few studies related to crypto exchanges have been conducted. Therefore, we plan to thoroughly study different types of crypto exchanges. Finally, we will develop applications to analyze the fund flows of illegal activities and automatically extract the funds’ path from the large transaction networks.

Availability of data and materials

Data and codes are available at https://github.com/fung2022/A-blockchain-oriented-approach-for-detecting-cyber-attack-transactions .

In order to facilitate readers understanding the concepts of blockchain, “Appendix 1 ” lists and explains the common concepts such as account, transaction, block, cryptocurrency, flash loan and decentralized exchange (DEX).

https://docs.uniswap.org/ .

https://aave.com/ .

https://pancakeswap.finance/ .

https://ethereum.org/en/ .

https://www.bnbchain.org/en .

https://solanaminer.com/ .

https://defiyield.app/rekt-database .

https://bscscan.com/ .

The ERC-20 introduces a standard for Fungible Tokens on Ethereum, in other words, they have a property that makes each token be exactly the same (in type and value) as another token.

The BEP-20 token standard serves pretty much the same function as the ERC-20 token standard, but it applies to tokens built on the Binance Smart Chain (BSC).

https://scikit-learn.org/stable/modules/g0.013enerated/sklearn.model_selection.GridSearchCV.html

Abbreviations

Decentralized applications

Binance smart chain

Decentralized exchange

Supervised machine learning methods

Postmortem analysis technology

Unsupervised machine learning methods

Random forest

Light gradient boosting machine

Graph convolutional network

Local outliers’ factors

Cluster-based local outlier factor

Histogram-based outlier score

One-class SVM

Variational autoencoder

Deep support vector data description

Feature bagging

• Extended isolation forest

Weighted and extended isolation forest

Externally owned account

Angiulli F, Pizzuti C (2002) Fast outlier detection in high dimensional spaces. In: European conference on principles of data mining and knowledge discovery, pp 15–27

Aspris A, Foley S, Svec J, Wang L (2021) Decentralized exchanges: the “wild west” of cryptocurrency trading. Int Rev Financ Anal 77:101845

Article   Google Scholar  

Aziz RM, Baluch MF, Patel S, Ganie AH (2022) LGBM: a machine learning approach for Ethereum fraud detection. Int J Inf Technol 1–11

Breiman L (2001) Random forests. Mach Learn 45(1):5–32

Breunig MM, Kriegel H-P, Ng RT, Sander J (2000) LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD international conference on management of data, pp 93–104

Carcillo F, Dal Pozzolo A, Le Borgne Y-A, Caelen O, Mazzer Y, Bontempi G (2018) Scarff: a scalable framework for streaming credit card fraud detection with spark. Inf Fus 41:182–194

Carcillo F, Le Borgne Y-A, Caelen O, Kessaci Y, Oblé F, Bontempi G (2021) Combining unsupervised and supervised learning in credit card fraud detection. Inf Sci 557:317–331

Chen T, Guestrin C (2016) Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 785–794

Dal Pozzolo A, Caelen O, Le Borgne Y-A, Waterschoot S, Bontempi G (2014) Learned lessons in credit card fraud detection from a practitioner perspective. Expert Syst Appl 41(10):4915–4928

Efanov D, Roschin P (2018) The all-pervasiveness of the blockchain technology. Procedia Comput Sci 123:116–121

Falcão F, Zoppi T, Silva CBV, Santos A, Fonseca B, Ceccarelli A, Bondavalli A (2019) Quantitative comparison of unsupervised anomaly detection algorithms for intrusion detection. In: Proceedings of the 34th ACM/SIGAPP symposium on applied computing, pp 318–327

Fang F, Ventre C, Basios M, Kanthan L, Martinez-Rego D, Wu F, Li L (2022) Cryptocurrency trading: a comprehensive survey. Financ Innov 8(1):1–59

Farrugia S, Ellul J, Azzopardi G (2020) Detection of illicit accounts over the Ethereum blockchain. Expert Syst Appl 150:113318

Goldstein M, Dengel A (2012) Histogram-based outlier score (HBOS): a fast unsupervised anomaly detection algorithm. In: KI-2012: poster and demo track, pp 59–63

Hariri S, Kind MC, Brunner RJ (2019) Extended isolation forest. IEEE Trans Knowl Data Eng 33(4):1479–1489

Harvey CR, Ramachandran A, Santoro J (2021) DeFi and the future of finance. Wiley

Google Scholar  

He Z, Xu X, Deng S (2003) Discovering cluster-based local outliers. Pattern Recogn Lett 24(9–10):1641–1650

Hilas CS, Mastorocostas PA (2008) An application of supervised and unsupervised learning approaches to telecommunications fraud detection. Knowl-Based Syst 21(7):721–726

Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning, pp 448–456

Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu T-Y (2017) Lightgbm: a highly efficient gradient boosting decision tree. In: Advances in neural information processing systems, vol 30

Kingma DP, Welling M (2013) Auto-encoding variational Bayes. arXiv preprint http://arxiv.org/abs/1312.6114

Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint http://arxiv.org/abs/1609.02907

Lazarevic A, Kumar V (2005) Feature bagging for outlier detection. In: Proceedings of the eleventh ACM SIGKDD international conference on knowledge discovery in data mining, pp 157–166

Liu FT, Ting KM, Zhou Z-H (2012) Isolation-based anomaly detection. ACM Trans Knowl Discov Data (TKDD) 6(1):1–39

Patel V, Pan L, Rajasegarar S (2020) Graph deep learning based anomaly detection in ethereum blockchain network. In: International conference on network and system security, pp 132–148

Pontes FJ, Amorim G, Balestrassi PP, Paiva A, Ferreira JR (2016) Design of experiments and focused grid search for neural network parameter optimization. Neurocomputing 186:22–34

Puggini L, McLoone S (2018) An enhanced variable selection and Isolation Forest based methodology for anomaly detection with OES data. Eng Appl Artif Intell 67:126–135

Qin K, Zhou L, Livshits B, Gervais A (2021) Attacking the defi ecosystem with flash loans for fun and profit. In: International conference on financial cryptography and data security, pp 3–32

Rovetta S, Suchacka G, Masulli F (2020) Bot recognition in a Web store: an approach based on unsupervised learning. J Netw Comput Appl 157:102577

Ruff L, Vandermeulen R, Goernitz N, Deecke L, Siddiqui SA, Binder A, Müller E, Kloft M (2018) Deep one-class classification. In: International conference on machine learning, pp 4393–4402

Schölkopf B, Platt JC, Shawe-Taylor J, Smola AJ, Williamson RC (2001) Estimating the support of a high-dimensional distribution. Neural Comput 13(7):1443–1471

Sebastião H, Godinho P (2021) Forecasting and trading cryptocurrencies with machine learning under changing market conditions. Financ Innov 7(1):1–30

Shen J, Zhou J, Xie Y, Yu S, Xuan Q (2021) Identity inference on blockchain using graph neural network. In: International conference on blockchain and trustworthy systems, pp 3–17

Syarif I, Prugel-Bennett A, Wills G (2016) SVM parameter optimization using grid search and genetic algorithm to improve classification performance. TELKOMNIKA (telecommun Comput Electron Control) 14(4):1502–1509

Thabtah F, Hammoud S, Kamalov F, Gonsalves A (2020) Data imbalance in classification: experimental evaluation. Inf Sci 513:429–441

Xu JJ (2016) Are blockchains immune to all malicious attacks? Financ Innov 2(1):1–9

Xu M, Chen X, Kou G (2019) A systematic review of blockchain. Financ Innov 5(1):1–14

Yu S, Jin J, Xie Y, Shen J, Xuan Q (2021) Ponzi scheme detection in ethereum transaction network. In: International conference on blockchain and trustworthy systems, pp 175–186

Download references

Acknowledgements

We greatly thank the discussions from Dr. Chao Liu and Dr. Baoqiang Zhan.

This work was supported by the National Natural Science Foundation of China (72171059, 71771041, 72121001), the Fundamental Research Funds for the Central Universities (FRFCU5710000220) and the Natural Science Foundation of Heilongjiang Province, China (No. YQ2020G003).

Author information

Authors and affiliations.

School of Economics and Management, Harbin Institute of Technology, Harbin, 150001, China

Zhiqi Feng, Yongli Li & Xiaochen Ma

You can also search for this author in PubMed   Google Scholar

Contributions

ZF: Conceptualization, methodology, visualization, software, validation, and data curation, and writing—original draft. YL: Writing—review and editing, conceptualization, supervision, validation. XM: Writing—review and editing, visualization, and data curation. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yongli Li .

Ethics declarations

Competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix 1: Common concepts on account-based blockchain

In this appendix, the common concepts on account-based blockchain, related to the analysis of cyber-attack, are shown as follow.

Account. There are two types of accounts: externally owned account ( EOA ) controlled by private key, and smart contract account controlled by their codes, described in whitepaper of Ethereum and BSC. For the EOA, it is made up of a cryptographic pair of keys: public and private. The public key and private key are similar to an online bank account and the corresponding password, and losing the private key is equivalent to losing the funds of the corresponding account. For the smart contract account, it is written in programming languages such as Solidity, and all of the smart contracts are executed on the blockchain. Applications can call the smart contract functions, change their state, and initiate transactions. Both account types have the ability to receive, hold and send the crypto assets, and interact with the deployed smart contracts.

Transaction. The transaction on the account-based blockchain refers to an action initiated by an externally owned account. In other words, an account is managed by a human, not a contract. A transaction is a signed data message sent from an externally owned account to another account on blockchain, e.g., so the recipient of a transaction has more crypto asset and the sender has less. It contains the information of the transaction sender and recipient, which are the amount of cryptocurrency to be transferred and the transaction fee the sender is willing to pay. Generally, an internal transfer is the consequence of smart contract logic that is triggered by an external transaction, where the transaction is transmitted from the EOA to the smart contract. Meanwhile, the execution of transaction with a smart contract may result in more transactions depending on the code of smart contract.

Transaction Fee. Transaction fee is also known as gas fee, and it is the fee paid to the nodes (miner) for executing transaction. When we transfer money on the account-based blockchain, the miner must pack our transaction and put it on the blockchain to complete the transaction. In this process, the nodes will consume computing resources, and the miner should be compensated. The gas fee only depends on the complexity of the transaction. Overall, the higher gas fee is consumed in the more complicated transaction. Meanwhile, the price of gas can be set by users, and the set price will affect the transaction speed. As miners give priority to transactions with high gas prices. If the transaction party sets the gas price too low, the speed of the transaction will slow down.

Block. A block is mined when added to the account-based network, once the consensus is reached. A transaction is said to be mined when it is included to the blockchain in a new block. Therefore, each block has several transactions. In order to preserve the historical transaction records, every new block contains a unique identifier of its parent block, this is how all of the blocks are linked in the blockchain. As a result, all of the blocks on the account-based blockchain are strictly ordered as well as the transactions within the blocks. According the Etherscan and BscScan, the average block times, referring to the times it takes to mine a new block of BSC and Ethereum, are 2.5 and 12 s respectively.

Cryptocurrency . The Ether ( ETH ) and BNB are the digital fuel for Ethereum and BSC, respectively, which is similar to the gasoline for our cars. For instance, transaction fee is an amount of computer power required in order to execution of the transaction, which is paid by ETH or BNB. Compared with the ETH and BNB, ERC-20 and BEP-20 Tokens are the most commonly used tokens on the Ethereum and BSC network, which are supported by the smart contract. According to the report of Etherscan and BscScan, there are more than 500,000 types of ERC20 Tokens on Ethereum and 2,568,483 types of BEP-20 Tokens on BSC, with over 100 billion dollars market capitalization.

Flash loan . Flash loan is one of the decentralized applications based on smart contracts. Because of the state reverting feature of Ethereum and BSC, the tools of flash loan are developed to enable the uncollateralized lending service. This type of loan service provides users an unsecured loan from lenders without intermediaries. The rule of flash loan is that the borrower must pay back the loan before the transaction ends. Otherwise, the transaction will be rejected and the smart contract reverses the transaction, and it's like the loan never happened in the first place on Ethereum and BSC.

Decentralized exchange (DEX). DEXs are blockchain-based applications that provide users the trading of crypto assets without intermediaries. It works entirely through automated algorithms based on a set of smart contracts. Unlike the centralized exchanges, DEXs do not allow for the exchange between crypto assets and fiat. Meanwhile, DEXs do not hold users’ crypto assets. Instead, users hold all their assets directly in their wallets all the time.

Appendix 2: Real-world examples of cyber-attack transaction

In this section, two real-world examples of cyber-attack transaction, referring to Flash loan attack and re-entrancy attack, are elaborated below.

Real-world example of Smart contract exploit. Taking the re-entrancy as an example, its brief process is shown in Fig.  14 , DAO is the victim contract which is marked with blue, and the malicious proxy contract is marked with gray. The detail process of re-entrancy attack contains several steps:

Step 1. Malicious contract calls the withdrawBalance function of DAO attempting to withdraw a certain amount A of ETH from an account containing a large amount B of ETH;

Step 2. The withdrawBalance function of DAO check that the withdrawal is valid if B > A.

Step 3. The withdrawBalance function of DAO transfers the requested A ETH to the malicious contract.

Step 4. This transfer triggers Fallback function of the malicious contract, which calls the withdrawBalance function of DAO again requesting a withdrawal of A ETH.

Step 5. The withdrawBalance function of DAO checks that the withdrawal is valid, since account balance of Malicious contract is still B and B > A.

Step 6. The withdrawBalance function of DAO transfers the requested A ETH to the malicious contract.

Step 7. The Fallback function of malicious contract returns without performing any action.

Step 8. The withdrawBalance function of DAO updates its state to reflect the withdrawal in step 6, reducing account balance of malicious contract to (B–A) ETH.

Real-world example of Flash loan attack. For example, the Flash loan attack happened on 18 February 2020 which is shown in Fig.  15 . First, the hacker obtained a flash loan of 7500 ETH from the bZx protocol and split the total amount of ETH into three parts (3518, 900 and 3082). Second, the hacker converted 3518 ETH to sUSD on Synthetix, the synthetic USD token (sUSD) is enabled by the Syntetix protocol and the sUSD were bought at the price of $1. Third, the hacker swapped 900 ETH in two batches for sUSD through Kyber. The first batch was sold for 540 ETH in KyberSwap and the second batch was sold 18 times for 20 ETH each in Kyber, effectively inflating the price of sUSD up to $2 in Kyber (By this way, the supply of sUSD will be decreased, and the total supply of ETH is increasing in Kyber. The ratio of the supply of ETH and the supply of sUSD in Kyber will rise, which makes the price of sUSD go up). After this operation, the hacker has finished all the preparatory work, such as accumulation of sUSD and inflating the price to the certain price in Kyber. Fourth, since bZx relies on Kyber for the real-time price feed, with the spiked sUSD/ETH price (This price is higher than the actual prices), the hacker started to attack the bZx by borrowing 6796 ETH with all the collection of sUSD. Finally, the hacker repaid the 7500 ETH flash loan back to bZx with a profit of 2378 ETH.

An illustration of the re-entrancy attack

Flowchart of bZx Flash loan attack scheme

Appendix 3: Algorithms of the standard isolation forest and its extension

In this section, the algorithms of the standard isolation forest and its extension are introduced as follow (See Tables 12 , 13 , 14 , 15 ).

Appendix 4: Analysis of feature importance based on LGBM

In this section, LGBM is also selected to carry out the analysis of feature importance for verifying the whether or not the result of feature importance depends the selection of SML methods. And the results of feature importance for LGBM are show in Figs.

Feature importance of Smart contract exploit based on LGBM

Feature importance of Flash loan attack based on LGBM

Feature importance of Identity theft based on LGBM

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Feng, Z., Li, Y. & Ma, X. Blockchain-oriented approach for detecting cyber-attack transactions. Financ Innov 9 , 81 (2023). https://doi.org/10.1186/s40854-023-00490-6

Download citation

Received : 17 August 2022

Accepted : 29 March 2023

Published : 02 May 2023

DOI : https://doi.org/10.1186/s40854-023-00490-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Cyber-attack detection
• Decentralized application
• Financial security

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Medical iot record security and blockchain: systematic review of milieu, milestones, and momentum.

1. Introduction

• We conducted an empirical examination of the transcending evolution of digital certificates for medical record preservation.
• We assessed the adoption of blockchain technology in enhancing data privacy in medical record transmission.
• We closely explored the significant impact of cybersecurity and generational networks in exposing sensitive and vital health records for unauthorized access.
• We highlighted security issues and provided advanced research directions for the improvement of medical record privacy and preservation by incorporating blockchain technology and allied security frameworks.

2. Review Methodology

• Articles published in reputable journals and conference proceedings;
• Articles published in the English language;
• Articles with publication dates within the last 10 years (2013–2023);
• Articles with titles and contents covering the stated review scope and objectives.

3. Digital Certificate and Evolution of Medical Data Preservation

4. blockchain adoption for health record security, 4.1. blockchain network types, 4.1.1. public blockchain, 4.1.2. private blockchain, 4.1.3. consortium blockchains, 4.2. blockchain consensus algorithms, 4.2.1. proof of work (pow), 4.2.2. proof of stake (pos), 4.2.3. delegated proof of stake (dpos), 4.2.4. proof of authority (poa), 4.2.5. practical byzantine fault tolerance (bft), 4.3. blockchain smart contracts, 4.4. summary of blockchain for medical iot, 5. ai–blockchain integration in medical iot record security, 5.1. traceable data and security, 5.2. transparent clinical trials and medical reportage, 5.3. tractable supply chain management, 5.4. trustworthy drug discovery, 5.5. tensile cross-platform interoperability for data exchange, 5.6. timely medical data fraud and forgery detection, 6. 5g-blockchain for preservation of secured medical data, 6.1. offline medical record preservation, 6.2. online medical record preservation, 6.3. use cases of 5g-blockchain for mir security, 7. blockchain milestones in mir security, 7.1. blockchain benefits and digitization of healthcare, 7.1.1. accuracy of health information, 7.1.2. accentuated interoperability of mir platforms, 7.1.3. authentic protection of health records, 7.1.4. alleviation of administrative and handling costs, 7.1.5. authorized global accessibility of mirs, 7.1.6. assured auditing process for medical data, 8. blockchain milieu for mir security and recommended possible solutions, 8.1. difficulty in information exchange, 8.2. data and privacy leakage, 8.3. debilitating and large storage requirements, 8.4. distinct technologies and protocol conformity standardization, 8.5. definition and regulation of roles in distributive data sharing, 8.6. depletion of distribution rights of patients to data exclusivity, 8.7. drug prescription platform difficulty, 8.8. data ownership rules and processes, 8.9. contemporary research issues and future directions, 9. conclusions, author contributions, conflicts of interest.

• Kruse, C.S.; Mileski, M.; Vijaykumar, A.G.; Viswanathan, S.V.; Suskandla, U.; Chidambaram, Y. Impact of electronic health records on long-term care facilities: Systematic review. JMIR Med. Inform. 2017 , 5 , e7958. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Dizon, M.A.C.; Upson, P.J. Laws of encryption: An emerging legal framework. Comput. Law Secur. Rev. 2021 , 43 , 105635. [ Google Scholar ] [ CrossRef ]
• Shafinah, K.; Ikram, M.M. File Security based on Pretty Good Privacy (PGP) Concept. Comput. Inf. Sci. 2011 , 4 , 10. [ Google Scholar ]
• Lampson, B.; Rivest, R. A Simple Distributed Security Infrastructure. MIT—Massachussetts Institute of Technology. 1996; Volume 12; p. 2006. Available online: https://people.csail.mit.edu/rivest/pubs/RL96.ver-1.1.html (accessed on 15 April 2024).
• Sharma, K.; Shrivastava, G. Public key infrastructure and trust of web based knowledge discovery. Int. J. Eng. Sci. Manag. 2014 , 4 , 56–60. [ Google Scholar ]
• Kaur, R.; Kaur, A. Digital signature. In Proceedings of the 2012 International Conference on Computing Sciences, Washington, DC, USA, 14–15 September 2012; pp. 295–301. [ Google Scholar ]
• Lin, C.H.; Yeh, Y.S.; Chien, S.P.; Lee, C.Y.; Chien, H.S. Generalized secure hash algorithm: SHA-X. In Proceedings of the 2011 IEEE EUROCON-International Conference on Computer as a Tool, Lisbon, Portugal, 27–29 April 2011; pp. 1–4. [ Google Scholar ]
• Myers, M.; Ankney, R.; Malpani, A.; Galperin, S.; Adams, C.X. 509 Internet Public Key Infrastructure Online Certificate Status Protocol-OCSP ; Technical Report; The Internet Society: Reston, VA, USA, 1999. [ Google Scholar ]
• Huang, L.C.; Chu, H.C.; Lien, C.Y.; Hsiao, C.H.; Kao, T. Privacy preservation and information security protection for patients’ portable electronic health records. Comput. Biol. Med. 2009 , 39 , 743–750. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Shen, N.; Bernier, T.; Sequeira, L.; Strauss, J.; Silver, M.P.; Carter-Langford, A.; Wiljer, D. Understanding the patient privacy perspective on health information exchange: A systematic review. Int. J. Med. Inform. 2019 , 125 , 1–12. [ Google Scholar ] [ CrossRef ]
• Hossein, K.M.; Esmaeili, M.E.; Dargahi, T.; Khonsari, A.; Conti, M. BCHealth: A novel blockchain-based privacy-preserving architecture for IoT healthcare applications. Comput. Commun. 2021 , 180 , 31–47. [ Google Scholar ] [ CrossRef ]
• Jin, H.; Luo, Y.; Li, P.; Mathew, J. A review of secure and privacy-preserving medical data sharing. IEEE Access 2019 , 7 , 61656–61669. [ Google Scholar ] [ CrossRef ]
• Raghav, N.; Bhola, A. Blockchain based privacy preservation in healthcare: A recent trends and challenges. Psychol. Educ. J. 2021 , 58 , 5315–5324. [ Google Scholar ]
• Balasubramanium, S.; Sivasankar, K.; Rajasekaran, M.P. A Survey on Data privacy and preservation using Blockchain in Healthcare organization. In Proceedings of the 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India, 4–5 March 2021; pp. 956–962. [ Google Scholar ]
• Sharma, A.; Kaur, S.; Singh, M. A comprehensive review on blockchain and Internet of Things in healthcare. Trans. Emerg. Telecommun. Technol. 2021 , 32 , e4333. [ Google Scholar ] [ CrossRef ]
• Ahmad, R.W.; Salah, K.; Jayaraman, R.; Yaqoob, I.; Ellahham, S.; Omar, M. The role of blockchain technology in telehealth and telemedicine. Int. J. Med. Inform. 2021 , 148 , 104399. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zhang, J.; Zhong, S.; Wang, T.; Chao, H.C.; Wang, J. Blockchain-based systems and applications: A survey. J. Internet Technol. 2020 , 21 , 1–14. [ Google Scholar ]
• Hesse, B.W.; Hansen, D.; Finholt, T.; Munson, S.; Kellogg, W.; Thomas, J.C. Social participation in health 2.0. Computer 2010 , 43 , 45–52. [ Google Scholar ] [ CrossRef ]
• Stark, B.; Gewald, H.; Lautenbacher, H.; Haase, U.; Ruff, S. Misuse of ‘Break-the-Glass’ Policies in Hospitals: Detecting Unauthorized Access to Sensitive Patient Health Data. In Research Anthology on Privatizing and Securing Data ; IGI Global: Hershey, PA, USA, 2021; pp. 1231–1256. [ Google Scholar ]
• Osundina, K.S. Unauthorized Disclosure of Medical Information, Patient Rights and Legal Consequences as It Affect Patients and Healthcare Providers. Available online: https://iiardjournals.org/404.php/ (accessed on 9 March 2024).
• Maksymiv, T.; Chaplinskyi, R. Ways of unauthorized access to medical data and approach to organize secure access using blockchain technology. In Proceedings of the 2020 10th International Conference on Advanced Computer Information Technologies (ACIT), Deggendorf, Germany, 16–18 September 2020; pp. 791–795. [ Google Scholar ]
• Keshta, I.; Odeh, A. Security and privacy of electronic health records: Concerns and challenges. Egypt. Inform. J. 2021 , 22 , 177–183. [ Google Scholar ] [ CrossRef ]
• Moher, D.; Liberati, A.; Tetzlaff, J.; Altman, D.G. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. BMJ 2009 , 6 , 264–269. [ Google Scholar ] [ CrossRef ]
• Foley Library. SPIDER: Mixed Methods Qualitative Research Questions ; Foley Library: Spokane, WA, USA, 2022. [ Google Scholar ]
• Liu, Q.; Liu, Y.; Luo, M.; He, D.; Wang, H.; Choo, K.K.R. The Security of Blockchain-Based Medical Systems: Research Challenges and Opportunities. IEEE Syst. J. 2022 , 16 , 5741–5752. [ Google Scholar ] [ CrossRef ]
• Fatima, N.; Agarwal, P.; Sohail, S.S. Security and Privacy Issues of Blockchain Technology in Health Care—A Review. In ICT Analysis and Applications ; Fong, S., Dey, N., Joshi, A., Eds.; Springer: Singapore, 2022; pp. 193–201. [ Google Scholar ]
• Wenhua, Z.; Qamar, F.; Abdali, T.A.N.; Hassan, R.; Jafri, S.T.A.; Nguyen, Q.N. Blockchain technology: Security issues, healthcare applications, challenges and future trends. Electronics 2023 , 12 , 546. [ Google Scholar ] [ CrossRef ]
• Harn, L.; Ren, J. Generalized digital certificate for user authentication and key establishment for secure communications. IEEE Trans. Wirel. Commun. 2011 , 10 , 2372–2379. [ Google Scholar ] [ CrossRef ]
• O’Brien, M.; Weir, G.R. Understanding digital certificates. In Proceedings of the 2nd International Conference on Cybercrime Forensics Education & Training, Kent, Canterbury, UK, 1–2 September 2008. [ Google Scholar ]
• Akinsanya, O.O.; Papadaki, M.; Sun, L. Current cybersecurity maturity models: How effective in healthcare cloud? In Proceedings of the CERC, Darmstadt, Germany, 29–30 March 2019; pp. 211–222. [ Google Scholar ]
• Dang, Q.H. Secure Hash Standard ; National Institute of Standards & Technology: Gaithersburg, MD, USA, 2015. [ Google Scholar ]
• Mathew, A. The Limits to Peer Production in Security Infrastructures: Technological and Regulatory Challenges to the PGP Web of Trust. In Proceedings of the Sixth European Multidisciplinary Conference on Global Internet Governance Actors, Regulations, Transactions and Strategies, Nicosia, Cyprus, 13–14 April 2022. [ Google Scholar ]
• Halpin, H. All that is Solid Melts into Air: Towards Decentralized Cryptographic Access Control. In Proceedings of the 17th International Conference on Availability, Reliability and Security, Vienna, Austria, 23–26 August 2022; pp. 1–6. [ Google Scholar ]
• Ferguson, N.; Schneier, B.; Kohno, T. Cryptography Engineering: Design Principles and Practical Applications ; John Wiley & Sons: Hoboken, NJ, USA, 2011. [ Google Scholar ]
• Chen, L.; Moody, D.; Regenscheid, A.; Robinson, A. Digital Signature Standard (DSS) ; National Institute of Standards and Technology: Gaithersburg, MD, USA, 2023. [ Google Scholar ] [ CrossRef ]
• Kerry, C.F.; Gallagher, P.D. Digital Signature Standard (DSS) ; FIPS PUB. 2013; pp. 186–192. Available online: https://en.wikipedia.org/wiki/Digital_Signature_Standard (accessed on 9 March 2024).
• Wang, X.; Yin, Y.L.; Yu, H. Finding collisions in the full SHA-1. In Proceedings of the Advances in Cryptology–CRYPTO 2005: 25th Annual International Cryptology Conference, Santa Barbara, CA, USA, 14–18 August 2005; Proceedings 25. Springer: Berlin/Heidelberg, Germany, 2005; pp. 17–36. [ Google Scholar ]
• Cortez, D.M.A.; Sison, A.M.; Medina, R.P. Cryptographic randomness test of the modified hashing function of SHA256 to address length extension attack. In Proceedings of the 2020 8th International Conference on Communications and Broadband Networking, Auckland, New Zealand, 15–18 April 2020; pp. 24–28. [ Google Scholar ]
• Bernstein, D.J.; Lange, T. Post-quantum cryptography. Nature 2017 , 549 , 188–194. [ Google Scholar ] [ CrossRef ]
• Singh, S.; Rathore, S.; Alfarraj, O.; Tolba, A.; Yoon, B. A framework for privacy-preservation of IoT healthcare data using Federated Learning and blockchain technology. Future Gener. Comput. Syst. 2022 , 129 , 380–388. [ Google Scholar ] [ CrossRef ]
• Omidian, H.; Omidi, Y. Blockchain in pharmaceutical life cycle management. Drug Discov. Today 2022 , 27 , 935–938. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Kumar, A.; Singh, A.K.; Ahmad, I.; Kumar Singh, P.; Verma, P.K.; Alissa, K.A.; Bajaj, M.; Ur Rehman, A.; Tag-Eldin, E. A novel decentralized blockchain architecture for the preservation of privacy and data security against cyberattacks in healthcare. Sensors 2022 , 22 , 5921. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Chen, Z.; Xu, W.; Wang, B.; Yu, H. A blockchain-based preserving and sharing system for medical data privacy. Future Gener. Comput. Syst. 2021 , 124 , 338–350. [ Google Scholar ] [ CrossRef ]
• Razzaq, A.; Mohsan, S.A.H.; Ghayyur, S.A.K.; Al-Kahtani, N.; Alkahtani, H.K.; Mostafa, S.M. Blockchain in Healthcare: A Decentralized Platform for Digital Health Passport of COVID-19 Based on Vaccination and Immunity Certificates. Healthcare 2022 , 10 , 2453. [ Google Scholar ] [ CrossRef ]
• Hasselgren, A.; Kralevska, K.; Gligoroski, D.; Pedersen, S.A.; Faxvaag, A. Blockchain in healthcare and health sciences—A scoping review. Int. J. Med. Inform. 2020 , 134 , 104040. [ Google Scholar ] [ CrossRef ]
• Alnafrani, M.; Acharya, S. SecureRx: A blockchain-based framework for an electronic prescription system with opioids tracking. Health Policy Technol. 2021 , 10 , 100510. [ Google Scholar ] [ CrossRef ]
• Pericàs-Gornals, R.; Mut-Puigserver, M.; Payeras-Capellà, M.M. Highly private blockchain-based management system for digital COVID-19 certificates. Int. J. Inf. Secur. 2022 , 21 , 1069–1090. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• EHDHE: Enhancing security of healthcare documents in IoT-enabled digital healthcare ecosystems using blockchain. Inf. Sci. 2023 , 629 , 703–718. [ CrossRef ]
• Jeong, S.; Shen, J.H.; Ahn, B. A Study on Smart Healthcare Monitoring Using IoT Based on Blockchain. Wirel. Commun. Mob. Comput. 2021 , 2021 , 9932091. [ Google Scholar ] [ CrossRef ]
• Saranya, R.; Murugan, A. A systematic review of enabling blockchain in healthcare system: Analysis, current status, challenges and future direction. Mater. Today Proc. 2023 , 80 , 3010–3015. [ Google Scholar ] [ CrossRef ]
• Sara Ait, B.; Aaroud, A.; Sabiri, K.; Rguibi, M.A.; Cherradi, B. Design and implementation of a New Blockchain-based digital health passport: A Moroccan case study. Inform. Med. Unlocked 2022 , 35 , 101125. [ Google Scholar ] [ CrossRef ]
• Bataineh, M.R.; Mardini, W.; Khamayseh, Y.M.; Yassein, M.M.B. Novel and Secure Blockchain Framework for Health Applications in IoT. IEEE Access 2022 , 10 , 14914–14926. [ Google Scholar ] [ CrossRef ]
• Wazid, M.; Bera, B.; Mitra, A.; Das, A.K.; Ali, R. Private blockchain-envisioned security framework for AI-enabled IoT-based drone-aided healthcare services. In Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond, New York, NY, USA, 25 September 2020; DroneCom ’20. pp. 37–42. [ Google Scholar ] [ CrossRef ]
• Schmeelk, S.; Kanabar, M.; Peterson, K.; Pathak, J. Electronic health records and blockchain interoperability requirements: A scoping review. JAMIA Open 2022 , 5 , ooac068. [ Google Scholar ] [ CrossRef ]
• Ghosh, P.K.; Chakraborty, A.; Hasan, M.; Rashid, K.; Siddique, A.H. Blockchain Application in Healthcare Systems: A Review. Systems 2023 , 11 , 38. [ Google Scholar ] [ CrossRef ]
• Purohit, S.; Calyam, P.; Alarcon, M.L.; Bhamidipati, N.R.; Mosa, A.; Salah, K. HonestChain: Consortium blockchain for protected data sharing in health information systems. Peer-to-Peer Netw. Appl. 2021 , 14 , 3012–3028. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zhang, D.; Wang, S.; Zhang, Y.; Zhang, Q.; Zhang, Y. A Secure and Privacy-Preserving Medical Data Sharing via Consortium Blockchain. Secur. Commun. Netw. 2022 , 2022 , 2759787. [ Google Scholar ] [ CrossRef ]
• Han, Y.; Zhang, Y.; Vermund, S.H. Blockchain Technology for Electronic Health Records. Int. J. Environ. Res. Public Health 2022 , 19 , 15577. [ Google Scholar ] [ CrossRef ]
• Mingxiao, D.; Xiaofeng, M.; Zhe, Z.; Xiangwei, W.; Qijun, C. A review on consensus algorithm of blockchain. In Proceedings of the 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Banff, AB, Canada, 5–8 October 2017; pp. 2567–2572. [ Google Scholar ] [ CrossRef ]
• Ajakwe, S.O.; Saviour, I.I.; Kim, J.H.; Kim, D.S.; Lee, J.M. BANDA: A Novel Blockchain-Assisted Network for Drone Authentication. In Proceedings of the 2023 Fourteenth International Conference on Ubiquitous and Future Networks (ICUFN), Virtual, 4–7 July 2023; pp. 120–125. [ Google Scholar ]
• Xiong, H.; Chen, M.; Wu, C.; Zhao, Y.; Yi, W. Research on Progress of Blockchain Consensus Algorithm: A Review on Recent Progress of Blockchain Consensus Algorithms. Future Internet 2022 , 14 , 47. [ Google Scholar ] [ CrossRef ]
• Nakamoto, S.; Bitcoin, A. A Peer-to-Peer Electronic Cash System. Bitcoin. 2008; Volume 4, p. 15. Available online: https://bitcoin.org/bitcoin.pdf (accessed on 12 March 2024).
• Ajakwe, S.O.; Kim, D.S.; Lee, J.M. Drone transportation system: Systematic review of security dynamics for smart mobility. IEEE Internet Things J. 2023 , 10 , 14462–14482. [ Google Scholar ] [ CrossRef ]
• Manolache, M.A.; Manolache, S.; Tapus, N. Decision Making using the Blockchain Proof of Authority Consensus. Procedia Comput. Sci. 2022 , 199 , 580–588. [ Google Scholar ] [ CrossRef ]
• Bamakan, S.M.H.; Motavali, A.; Babaei Bondarti, A. A survey of blockchain consensus algorithms performance evaluation criteria. Expert Syst. Appl. 2020 , 154 , 113385. [ Google Scholar ] [ CrossRef ]
• Mendoza Arvizo, A.I.; Avelar Sosa, L.; García Alcaraz, J.L.; Cruz-Mejía, O. Beneficiary Contracts on a Lightweight Blockchain Architecture Using Smart Contracts: A Smart Healthcare System for Medical Records. Appl. Sci. 2023 , 13 , 6694. [ Google Scholar ] [ CrossRef ]
• Chinnasamy, P.; Albakri, A.; Khan, M.; Raja, A.A.; Kiran, A.; Babu, J.C. Smart Contract-Enabled Secure Sharing of Health Data for a Mobile Cloud-Based E-Health System. Appl. Sci. 2023 , 13 , 3970. [ Google Scholar ] [ CrossRef ]
• Igboanusi, I.S.; Nnadiekwe, C.A.; Ogbede, J.U.; Kim, D.S.; Lensky, A. BOMS: Blockchain-enabled organ matching system. Sci. Rep. 2024 , 14 , 16069. [ Google Scholar ] [ CrossRef ]
• Chinnasamy, P.; Vinodhini, B.; Praveena, V.; Vinothini, C.; Sujitha, B.B. Blockchain based Access Control and Data Sharing Systems for Smart Devices. J. Physics Conf. Ser. 2021 , 1767 , 012056. [ Google Scholar ] [ CrossRef ]
• Igboanusi, I.S.; Dirgantoro, K.P.; Lee, J.M.; Kim, D.S. Blockchain side implementation of Pure Wallet (PW): An offline transaction architecture. ICT Express 2021 , 7 , 327–334. [ Google Scholar ] [ CrossRef ]
• Ajakwe, S.O.; Ajakwe, I.U.; Taesoo, J.S.; Kim, D.S.; Lee, J.M. CIS-WQMS: Connected intelligence smart water quality monitoring scheme. Internet of Things 2023 , 23 , 100800–100819. [ Google Scholar ] [ CrossRef ]
• Alabdulatif, A.; Khalil, I.; Saidur Rahman, M. Security of Blockchain and AI-Empowered Smart Healthcare: Application-Based Analysis. Appl. Sci. 2022 , 12 , 11039. [ Google Scholar ] [ CrossRef ]
• Sinha, A.; Patel, A.; Jagdish, M. Application of Blockchain in Healthcare ; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2022. [ Google Scholar ] [ CrossRef ]
• Mancer, M.; Akram, K.M.; Barka, E.; Okba, K.; Sihem, S.; Harous, S.; Athamena, B.; Houhamdi, Z. Blockchain Technology for Secure Shared Medical Data. In Proceedings of the 2022 International Arab Conference on Information Technology (ACIT), Abu Dhabi, United Arab Emirates, 22–24 November 2022; pp. 1–6. [ Google Scholar ] [ CrossRef ]
• Dini, M.A.; Ajakwe, S.O.; Saviour, I.I.; Ihekoronye, V.U.; Nwankwo, O.U.; Uchechi, I.U.; Haryadi, G.A.; Putra, M.A.P.; Kim, D.S.; Jun, T.; et al. Patient-centric blockchain framework for secured medical record fidelity and authorization. In Proceedings of the Korean Institute of Communication and Sciences Summer Conference, Jeju Island, Republic of Korea, 21–24 June 2023; pp. 300–301. [ Google Scholar ]
• Awasthi, C.; Nawal, M.; Mishra, P.K. Security Concerns of Fog Computing in Field of Healthcare using Blockchain: A Review. In Proceedings of the 2021 International Conference on Communication information and Computing Technology (ICCICT), Mumbai, India, 25–27 June 2021. [ Google Scholar ] [ CrossRef ]
• Ajakwe, S.O.; Nwakanma, C.I.; Kim, D.S.; Lee, J.M. Key Wearable Device Technologies Parameters for Innovative Healthcare Delivery in B5G Network: A Review. IEEE Access 2022 , 10 , 49956–49974. [ Google Scholar ] [ CrossRef ]
• Hu, J.; Zhu, P.; Qi, Y.; Zhu, Q.; Li, X. A patent registration and trading system based on blockchain. Expert Syst. Appl. 2022 , 201 , 117094. [ Google Scholar ] [ CrossRef ]
• Anwar, A.; Goyal, S.B.; Ghosh, A. Tracking Clinical Trials and Enhancement of Security Control with Blockchain for Medical Record. In Proceedings of the 2021 IEEE 6th International Conference on Computing, Communication and Automation (ICCCA), Arad, Romania, 17–19 December 2021; pp. 632–636. [ Google Scholar ] [ CrossRef ]
• Rahman, M.A.; Hossain, M.S.; Islam, M.S.; Alrajeh, N.A.; Muhammad, G. Secure and Provenance Enhanced Internet of Health Things Framework: A Blockchain Managed Federated Learning Approach. IEEE Access 2020 , 8 , 205071–205087. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Curbera, F.; Dias, D.M.; Simonyan, V.; Yoon, W.A.; Casella, A. Blockchain: An enabler for healthcare and life sciences transformation. IBM J. Res. Dev. 2019 , 63 , 8:1–8:9. [ Google Scholar ] [ CrossRef ]
• Hang, L.; Chen, C.; Zhang, L.; Yang, J. Blockchain for applications of clinical trials: Taxonomy, challenges, and future directions. IET Commun. 2022 , 16 , 2371–2393. [ Google Scholar ] [ CrossRef ]
• Zhavoronkov, A.; Ivanenkov, Y.A.; Aliper, A.; Veselov, M.S.; Aladinskiy, V.A.; Aladinskaya, A.V.; Terentiev, V.A.; Polykovskiy, D.A.; Kuznetsov, M.D.; Asadulaev, A.; et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat. Biotechnol. 2019 , 37 , 1038–1040. [ Google Scholar ] [ CrossRef ]
• Xia, Q.; Sifah, E.B.; Asamoah, K.O.; Gao, J.; Du, X.; Guizani, M. MeDShare: Trust-Less Medical Data Sharing Among Cloud Service Providers via Blockchain. IEEE Access 2017 , 5 , 14757–14767. [ Google Scholar ] [ CrossRef ]
• Linn, L.A.; Martha B. Koo, M. Blockchain For Health Data and Its Potential Use in Health IT and Health Care Related Research. In Proceedings of the ONC/NIST Use of Blockchain for Healthcare and Research Workshop, Gaithersburg, MD, USA, 7 September 2016; Volume 17, pp. 1–10. [ Google Scholar ]
• Bocek, T.; Rodrigues, B.B.; Strasser, T.; Stiller, B. Blockchains everywhere—A use-case of blockchains in the pharma supply-chain. In Proceedings of the 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), Lisbon, Portugal, 8–12 May 2017; pp. 772–777. [ Google Scholar ] [ CrossRef ]
• Nawale, S.D.; Konapure, R.R. Blockchain & IoT based Drugs Traceability for Pharma Industry. In Proceedings of the 2021 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), Cardiff, UK, 21–23 June 2021; pp. 1–4. [ Google Scholar ] [ CrossRef ]
• Niu, B.; Dong, J.; Liu, Y. Incentive alignment for blockchain adoption in medicine supply chains. Transp. Res. Part E Logist. Transp. Rev. 2021 , 152 , 102276. [ Google Scholar ] [ CrossRef ]
• Ajakwe, S.O.; Arkter, R.; Ahakonye, L.A.C.; Kim, D.S.; Lee, J.M. Real-Time Monitoring of COVID-19 Vaccination Compliance: A Ubiquitous IT Convergence Approach. In Proceedings of the 2021 International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Republic of Korea, 20–22 October 2021; pp. 440–445. [ Google Scholar ] [ CrossRef ]
• Khatter, K.D. Non-functional requirements for blockchain enabled medical supply chain. Int. J. Syst. Assur. Eng. Manag. 2022 , 13 , 1219–1231. [ Google Scholar ] [ CrossRef ]
• Kumar, B.; Mohanraj, T.; ShahulHammed, S.; Santhosh, R. A Study of BlockChain Technologies and health Care Systems. In Proceedings of the 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India, 7–9 October 2020; pp. 265–267. [ Google Scholar ] [ CrossRef ]
• Omar, I.A.; Jayaraman, R.; Debe, M.S.; Salah, K.; Yaqoob, I.; Omar, M. Automating procurement contracts in the healthcare supply chain using blockchain smart contracts. IEEE Access 2021 , 9 , 37397–37409. [ Google Scholar ] [ CrossRef ]
• Premkumar, A.; Srimathi, C. Application of Blockchain and IoT towards Pharmaceutical Industry. In Proceedings of the 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 6–7 March 2020; pp. 729–733. [ Google Scholar ] [ CrossRef ]
• Abbas, K.; Afaq, M.; Ahmed Khan, T.; Song, W.C. A Blockchain and Machine Learning-Based Drug Supply Chain Management and Recommendation System for Smart Pharmaceutical Industry. Electronics 2020 , 9 , 852. [ Google Scholar ] [ CrossRef ]
• Ramdasani, U.; Vinzuda, G.; Tanwar, S.; Gupta, R.; Guizani, M. DuBloQ: Blockchain and Q-Learning Based Drug Discovery in Healthcare 4.0. In Proceedings of the 2022 International Wireless Communications and Mobile Computing (IWCMC), Dubrovnik, Croatia, 30 May–3 June 2022; pp. 284–289. [ Google Scholar ] [ CrossRef ]
• Yuksel, M.; Dogac, A. Interoperability of Medical Device Information and the Clinical Applications: An HL7 RMIM based on the ISO/IEEE 11073 DIM. IEEE Trans. Inf. Technol. Biomed. 2011 , 15 , 557–566. [ Google Scholar ] [ CrossRef ]
• Shukla, M.; Lin, J.; Seneviratne, O. BlockIoT: Blockchain-based health data integration using IoT devices. In Proceedings of the AMIA Annual Symposium Proceedings. American Medical Informatics Association, San Diego, CA, USA, 30 October–3 November 2021; Volume 2021, p. 1119. [ Google Scholar ]
• Jain, S.; Anand, A.; Gupta, A.; Awasthi, K.; Gujrati, S.; Channegowda, J. Blockchain and Machine Learning in Health Care and Management. In Proceedings of the 2020 International Conference on Mainstreaming Block Chain Implementation (ICOMBI), Bengaluru, India, 21–22 February 2020; pp. 1–5. [ Google Scholar ] [ CrossRef ]
• Yaeger, K.; Martini, M.; Rasouli, J.; Costa, A. Emerging blockchain technology solutions for modern healthcare infrastructure. J. Sci. Innov. Med. 2019 , 2 . [ Google Scholar ] [ CrossRef ]
• Nichol, P.B.; Brandt, J. Co-creation of trust for healthcare: The cryptocitizen framework for interoperability with blockchain. Res. Propos. 2016 , 1–9. [ Google Scholar ] [ CrossRef ]
• Saldamli, G.; Reddy, V.; Bojja, K.S.; Gururaja, M.K.; Doddaveerappa, Y.; Tawalbeh, L. Health care insurance fraud detection using blockchain. In Proceedings of the 2020 Seventh international conference on software defined systems (SDS), Paris, France, 20–23 April 2020; pp. 145–152. [ Google Scholar ]
• Ihekoronye, V.U.; Ajakwe, S.O.; Kim, D.S.; Lee, J.M. Cyber Edge Intelligent Intrusion Detection Framework For UAV Network Based on Random Forest Algorithm. In Proceedings of the 2022 13th International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Republic of Korea, 19–21 October 2022; pp. 1242–1247. [ Google Scholar ] [ CrossRef ]
• McGhin, T.; Choo, K.K.R.; Liu, C.Z.; He, D. Blockchain in healthcare applications: Research challenges and opportunities. J. Netw. Comput. Appl. 2019 , 135 , 62–75. [ Google Scholar ] [ CrossRef ]
• Liu, W.; Yu, Q.; Li, Z.; Li, Z.; Su, Y.; Zhou, J. A blockchain-based system for anti-fraud of healthcare insurance. In Proceedings of the 2019 IEEE 5th International Conference on Computer and Communications (ICCC), Chengdu, China, 6–9 December 2019; pp. 1264–1268. [ Google Scholar ]
• Ajakwe, S.O.; Ihekoronye, V.U.; Ajakwe, I.U.; Jun, T.; Kim, D.S.; Lee, J.M. Connected Intelligence for Smart Water Quality Monitoring System in IIoT. In Proceedings of the 2022 13th International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Republic of Korea, 19–21 October 2022; pp. 2386–2391. [ Google Scholar ] [ CrossRef ]
• Sharma, M.; Goel, A.K.; Singhal, P. Explainable AI Driven Applications for Patient Care and Treatment. In Explainable AI: Foundations, Methodologies and Applications ; Springer: Berlin/Heidelberg, Germany, 2022; pp. 135–156. [ Google Scholar ]
• Farrugia, D.; Zerafa, C.; Cini, T.; Kuasney, B.; Livori, K. A real-time prescriptive solution for explainable cyber-fraud detection within the iGaming industry. Sn Comput. Sci. 2021 , 2 , 215. [ Google Scholar ] [ CrossRef ]
• Jain, G.; Jain, A. 22—Blockchain for 5G-enabled networks in healthcare service based on several aspects. In Blockchain Applications for Healthcare Informatics ; Tanwar, S., Ed.; Academic Press: Cambridge, MA, USA, 2022; pp. 471–493. [ Google Scholar ] [ CrossRef ]
• Hewa, T.; Braeken, A.; Ylianttila, M.; Liyanage, M. Multi-Access Edge Computing and Blockchain-based Secure Telehealth System Connected with 5G and IoT. In Proceedings of the GLOBECOM 2020—2020 IEEE Global Communications Conference, Taipei, Taiwan, 7–11 December 2020. [ Google Scholar ] [ CrossRef ]
• Cameron, L. Keeping Your Medical Records Safe in the Cloud: Researchers Analyze Blockchain as a Solution. IEEE Comput. Soc. 2020 , 18 . Available online: https://www.computer.org/publications/tech-news/research/blockchain-health-medical-records-cloud-security (accessed on 20 May 2024).
• Patel, V. A framework for secure and decentralized sharing of medical imaging data via blockchain consensus. Health Inform. J. 2018 , 25 , 146045821876969. [ Google Scholar ] [ CrossRef ]
• Executive, N. Information for Health: An Information Strategy for the Modern NHS 1998–2005: A National Strategy for Local Implementation ; NHS/Department of Health: Leeds, UK, 1998. [ Google Scholar ]
• Madine, M.M.; Battah, A.A.; Yaqoob, I.; Salah, K.; Jayaraman, R.; Al-Hammadi, Y.; Pesic, S.; Ellahham, S. Blockchain for Giving Patients Control Over Their Medical Records. IEEE Access 2020 , 8 , 193102–193115. [ Google Scholar ] [ CrossRef ]
• Hombal, U.; Dayananda, R.B. A Review on Security and Privacy Preserving Mechanisms of Electronic Health Records in Cloud. In Proceedings of the 2021 Asian Conference on Innovation in Technology (ASIANCON), Pune, India, 27–29 August 2021; pp. 1–4. [ Google Scholar ] [ CrossRef ]
• Bibal Benifa, J.; Venifa Mini, G.; Krishnan, S. Chapter 14—Blockchain-based health care monitoring for privacy preservation of COVID-19 medical records. In Blockchain for Smart Cities ; Krishnan, S., Balas, V.E., Julie, E.G., Robinson, Y.H., Kumar, R., Eds.; Elsevier: Amsterdam, The Netherlands, 2021; pp. 259–294. [ Google Scholar ] [ CrossRef ]
• Zou, R.; Lv, X.; Zhao, J. SPChain: Blockchain-based medical data sharing and privacy-preserving eHealth system. Inf. Process. Manag. 2021 , 58 , 102604. [ Google Scholar ] [ CrossRef ]
• Mondal, S.; Shafi, M.; Gupta, S.; Gupta, S.K. Blockchain based secure architecture for electronic healthcare record management. GMSARN Int. J. 2022 , 16 , 413–426. [ Google Scholar ]
• Johari, R.; Kumar, V.; Gupta, K.; Vidyarthi, D.P. BLOSOM: BLOckchain technology for Security of Medical records. ICT Express 2022 , 8 , 56–60. [ Google Scholar ] [ CrossRef ]
• Mhamdi, H.; Ayadi, M.; Ksibi, A.; Al-Rasheed, A.; Soufiene, B.O.; Hedi, S. SEMRAchain: A secure electronic medical record based on blockchain technology. Electronics 2022 , 11 , 3617. [ Google Scholar ] [ CrossRef ]
• Oladele, J.K.; Ojugo, A.A.; Odiakaose, C.C.; Emordi, F.U.; Abere, R.A.; Nwozor, B.; Ejeh, P.O.; Geteloma, V.O. BEHeDaS: A Blockchain Electronic Health Data System for Secure Medical Records Exchange. J. Comput. Theor. Appl. 2024 , 1 , 231–242. [ Google Scholar ] [ CrossRef ]
• Usman, M.; Qamar, U. Secure Electronic Medical Records Storage and Sharing Using Blockchain Technology. Procedia Comput. Sci. 2020 , 174 , 321–327. [ Google Scholar ] [ CrossRef ]
• Factom Announces Partnership with Healthnautica. 2015. Available online: https://www.prweb.com/releases/2015/04/prweb12673607.htm (accessed on 12 April 2024).
• Zhang, P.; White, J.; Schmidt, D.C.; Lenz, G.; Rosenbloom, S.T. FHIRChain: Applying Blockchain to Securely and Scalably Share Clinical Data. Comput. Struct. Biotechnol. J. 2018 , 16 , 267–278. [ Google Scholar ] [ CrossRef ]
• Du, M.; Chen, Q.; Chen, J.; Ma, X. An Optimized Consortium Blockchain for Medical Information Sharing. IEEE Trans. Eng. Manag. 2021 , 68 , 1677–1689. [ Google Scholar ] [ CrossRef ]
• Khatoon, A. A Blockchain-Based Smart Contract System for Healthcare Management. Electronics 2020 , 9 , 94. [ Google Scholar ] [ CrossRef ]
• Liu, X.; Wang, Z.; Jin, C.; Li, F.; Li, G. A Blockchain-Based Medical Data Sharing and Protection Scheme. IEEE Access 2019 , 7 , 118943–118953. [ Google Scholar ] [ CrossRef ]
• Yaqoob, I.; Salah, K.; Jayaraman, R.; Al-Hammadi, Y. Blockchain for Healthcare Data Management: Opportunities, Challenges, and Future Recommendations. Neural Comput. Appl. 2022 , 34 , 11475–11490. [ Google Scholar ] [ CrossRef ]
• Cornelius, C.A.; Qusay, H.M.; J. Mikael, E. Blockchain Technology in Healthcare: A Systematic Review. Healthcare 2019 , 7 , 56. [ Google Scholar ] [ CrossRef ]
• Wang, S.; Wang, J.; Wang, X.; Qiu, T.; Yuan, Y.; Ouyang, L.; Guo, Y.; Wang, F.Y. Blockchain-Powered Parallel Healthcare Systems Based on the ACP Approach. IEEE Trans. Comput. Soc. Syst. 2018 , 5 , 942–950. [ Google Scholar ] [ CrossRef ]
• Khan, W.A.; Khattak, A.M.; Hussain, M.; Amin, M.B.; Afzal, M.; Nugent, C.; Lee, S. An adaptive semantic based mediation system for data interoperability among Health Information Systems. J. Med. Syst. 2014 , 38 , 28. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Reisman, M. EHRs: The Challenge of Making Electronic Data Usable and Interoperable. Pharm. Ther. 2017 , 42 , 572–575. [ Google Scholar ]
• Dagher, G.G.; Mohler, J.; Milojkovic, M.; Marella, P.B. Ancile: Privacy-preserving framework for access control and interoperability of electronic health records using blockchain technology. Sustain. Cities Soc. 2018 , 39 , 283–297. [ Google Scholar ] [ CrossRef ]
• Alam Khan, F.; Asif, M.; Ahmad, A.; Alharbi, M.; Aljuaid, H. Blockchain technology, improvement suggestions, security challenges on smart grid and its application in healthcare for sustainable development. Sustain. Cities Soc. 2020 , 55 , 102018. [ Google Scholar ] [ CrossRef ]
• Vazirani, A.; o’Donoghue, O.; Brindley, D.; Meinert, E. Blockchain vehicles for efficient Medical Record management. NPJ Digit. Med. 2020 , 3 , 1. [ Google Scholar ] [ CrossRef ]
• Kassab, M.; DeFranco, J.; Destefanis, G.; Graciano Neto, V. Exploring Research in Blockchain for Healthcare and a Roadmap for the Future. IEEE Trans. Emerg. Top. Comput. 2019 , 9 , 1835–1852. [ Google Scholar ] [ CrossRef ]
• Kumar, T.; Ramani, V.; Ahmad, I.; Braeken, A.; Harjula, E.; Ylianttila, M. Blockchain Utilization in Healthcare: Key Requirements and Challenges. In Proceedings of the 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom), Ostrava, Czech Republic, 17–20 September 2018; pp. 1–7. [ Google Scholar ] [ CrossRef ]
• Vonitsanos, G.; Panagiotakopoulos, T.; Kanavos, A. Issues and Challenges of using Blockchain for IoT Data Management in Smart Healthcare. Biomed. J. Sci. Tech. Res. 2021 , 40 , 32052–32057. [ Google Scholar ]
• Boulos, M.N.K.; Wilson, J.T.; Clauson, K.A. Geospatial blockchain: Promises, challenges, and scenarios in health and healthcare. Int. J. Health Geogr. 2018 , 17 , 25. [ Google Scholar ] [ CrossRef ]
• Igboanusi, I.S.; Allwinnaldo, A.; Alief, R.N.; Ansori, M.R.R.; Lee, J.M.; Kim, D.S. Smart auto mining (SAM) for industrial IoT blockchain network. IET Commun. 2022 , 16 , 2123–2132. [ Google Scholar ] [ CrossRef ]
• Stagnaro, C. White Paper: Innovative Blockchain Uses in Health Care; Freed Associate. 2017. Available online: https://s3.amazonaws.com/arena-attachments/1649918/a272c30523a39678dadcddc272d53a24.pdf?1516914525 (accessed on 5 May 2024).
• Ratta, P.; Kaur, A.; Sharma, S.; Shabaz, M.; Dhiman, G. Application of Blockchain and Internet of Things in Healthcare and Medical Sector: Applications, Challenges, and Future Perspectives. J. Food Qual. 2021 , 2021 , 1–20. [ Google Scholar ] [ CrossRef ]
• Deepa, M.; Roshni Naveena, S.; Harini, N.D.; Sravika, V.; Soundarya, S.; Reshma, S. A Novel Electronic Medical Record Design Using Cryptography and Steganography Techniques. In Proceedings of the 2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, 2–4 December 2021; pp. 377–382. [ Google Scholar ] [ CrossRef ]
• Yu, Y.; Li, Q.; Zhang, Q.; Hu, W.; Liu, S. Blockchain-Based Multi-Role Healthcare Data Sharing System. In Proceedings of the 2020 IEEE International Conference on E-Health Networking, Application & Services (HEALTHCOM), Virtual Conference, 1–2 March 2021; pp. 1–6. [ Google Scholar ] [ CrossRef ]
• Salim, M.M.; Park, J.H. Federated learning-based secure electronic health record sharing scheme in medical informatics. IEEE J. Biomed. Health Inform. 2022 , 27 , 617–624. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ali, S.; Armand, T.P.T.; Athar, A.; Hussain, A.; Ali, M.; Yaseen, M.; Joo, M.I.; Kim, H.C. Metaverse in Healthcare Integrated with Explainable AI and Blockchain: Enabling Immersiveness, Ensuring Trust, and Providing Patient Data Security. Sensors 2023 , 23 , 565. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Shurrab, S.; Duwairi, R. Self-supervised learning methods and applications in medical imaging analysis: A survey. PeerJ Comput. Sci. 2022 , 8 , e1045. [ Google Scholar ] [ CrossRef ] [ PubMed ]

Click here to enlarge figure

Share and Cite

Ajakwe, S.O.; Saviour, I.I.; Ihekoronye, V.U.; Nwankwo, O.U.; Dini, M.A.; Uchechi, I.U.; Kim, D.-S.; Lee, J.M. Medical IoT Record Security and Blockchain: Systematic Review of Milieu, Milestones, and Momentum. Big Data Cogn. Comput. 2024 , 8 , 121. https://doi.org/10.3390/bdcc8090121

Ajakwe SO, Saviour II, Ihekoronye VU, Nwankwo OU, Dini MA, Uchechi IU, Kim D-S, Lee JM. Medical IoT Record Security and Blockchain: Systematic Review of Milieu, Milestones, and Momentum. Big Data and Cognitive Computing . 2024; 8(9):121. https://doi.org/10.3390/bdcc8090121

Ajakwe, Simeon Okechukwu, Igboanusi Ikechi Saviour, Vivian Ukamaka Ihekoronye, Odinachi U. Nwankwo, Mohamed Abubakar Dini, Izuazu Urslla Uchechi, Dong-Seong Kim, and Jae Min Lee. 2024. "Medical IoT Record Security and Blockchain: Systematic Review of Milieu, Milestones, and Momentum" Big Data and Cognitive Computing 8, no. 9: 121. https://doi.org/10.3390/bdcc8090121

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

• DOI: 10.1109/MobiSecServ58080.2023.10329210
• Corpus ID: 265524883

Analysis of Blockchain Security: Classic Attacks, Cybercrime and Penetration Testing

• Shreshta Kaushik , Nour El Madhoun
• Published in Eighth International… 4 November 2023
• Computer Science
• 2023 Eighth International Conference On Mobile And Secure Services (MobiSecServ)

Figures from this paper

2 Citations

Blockchain technology in the healthcare sector: overview and security analysis, a blockchain framework for efficient resource allocation in edge computing, 35 references, a comparison of blockchain application and security issues from bitcoin to cybersecurity, a survey of blockchain from the perspectives of applications, challenges, and opportunities, blockchain challenges and opportunities: a survey, a comprehensive review of denial of service attacks in blockchain ecosystem and open challenges, security and privacy on blockchain, analysis of security in blockchain: case study in 51%-attack detecting, blockchain-based approach to thwart replay attacks targeting remote keyless entry systems, penetration testing framework for smart contract blockchain, block-hash of blockchain framework against man-in-the-middle attacks, a tractable probabilistic approach to analyze sybil attacks in sharding-based blockchain protocols, related papers.

Showing 1 through 3 of 0 Related Papers

Future Cyber Threats to Central Banks: Projecting the Evolution of Financial Cyberattacks in a Quantum and AI-Driven World

• August 2024

Discover the world's research

• 25+ million members
• 160+ million publication pages
• 2.3+ billion citations

• Martin Libicki

• T Armerding
• A Giandomenico
• Recruit researchers
• Join for free
• Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up