Blockchain System Research Articles

Integration of NLP and machine learning methods for smart contract security: a comparison with traditional approaches

In modern blockchain systems, smart contracts are one of the most critical components for ensuring the automated execution of agreements without the need for intermediaries. However, smart contracts written in languages like Solidity may contain vulnerabilities that can be exploited by malicious actors to steal funds or manipulate assets. Given the increasing number of attacks on smart contracts, the development of effective methods for detecting such vulnerabilities is crucial. Traditional approaches to detecting vulnerabilities in smart contracts include symbolic execution, fuzzing, formal verification, and pattern matching. These methods have their advantages but face several challenges, such as high resource consumption, limitations in detecting new types of vulnerabilities, and difficulties in scaling to large contracts. As a result, there is a need to introduce new approaches, such as natural language processing (NLP) and machine learning, which can address these challenges more effectively. In this study, an NLP-based method was explored, using Word2Vec to convert smart contract code into vector representations, allowing for better analysis of the semantic relationships between elements of the code. These vector representations are then fed into a bidirectional recurrent neural network with GRU blocks and an attention mechanism. This approach allows the model to focus on the most important parts of the code and improve the accuracy of vulnerability detection. The comparative analysis showed that NLP-based methods significantly outperform traditional approaches in all key metrics. In particular, the GRU model with an attention mechanism demonstrated high results in accuracy, recall, and F-measure, making it effective for detecting complex vulnerabilities such as reentrancy. Furthermore, the NLP-based approach is capable of adapting to new types of attacks thanks to training on large datasets. Thus, the integration of NLP and machine learning represents a promising direction for enhancing the security of smart contracts. Future research can focus on improving these approaches, particularly through the implementation of advanced models such as transformers.

A COMPARATIVE ANALYSIS OF LEGAL FRAMEWORK FOR DATA PROTECTION IN GLOBAL JURISDICTIONS

Along with the advancement in modern technology, the dependency of people on technology increased. Along with the passage of time, technology became so advanced that crimes entered the realm of cybercrimes. The article highlights the different types of data along with the basic criterion of data protection. This article refers to the critical analysis of global legislations of different states on data protection, how they intend to protect data, how they deal with the crimes such as theft, breach and misuse, and the loopholes in the legislation. All legislations related to data and their methods to tackle the data breach are endorsed, and their loopholes have been highlighted. Though every state has enacted legislation to protect data at its best, a global law is needed to protect data at the macro level. These crimes affect the individuals in the territory of a state but also make the international community vulnerable. There is a need to be proper provisions and clearly stating the jurisdiction and scope of these legislations. The blockchain system is an emerging technology system and can be used effectively to achieve the goal. Data protection is the right of every individual, and it should be preserved.

Application of edge computing and IoT technology in supply chain finance

This study proposes an IoT data management framework based on blockchain and edge computing and conducts detailed simulation experiment design and performance evaluation for the field of supply chain finance. Previous methods often struggled with high latency, limited scalability, and inadequate risk management, and to address these issues, the experimental platform includes traditional centralized systems, distributed systems, blockchain-based systems, and edge computing + blockchain systems. By monitoring indicators such as data processing delay, data transmission delay, data retrieval time, system throughput, and data integrity of each platform, and introducing evaluation formulas for credit risk, operational risk, and market risk, the performance of different systems in processing supply chain financial data is comprehensively analyzed. The experimental results show that the edge computing + blockchain system performs well in data processing efficiency, security, real-time, and system throughput, especially under high load conditions. Our market risk value is reduced to 0.015. This framework improves the efficiency and security of data transmission and effectively reduces credit risk and operational risk, providing an efficient and reliable solution for data management in supply chain finance.

Evaluation of patients' perception of a blockchain system for prescription management

The management of medical prescriptions without computational system can result in errors and security issues. This study, conducted in Monclova, Coahuila, evaluates the perception and disposition of patients from public and private health services towards implementing a secure computational system to improve prescription and medical record management. A survey of 68 adults, using a mixed research methodology. The results indicate that a secure and low-cost computational system would have positive acceptance among the population, thereby validating the researcher's hypothesis. In conclusion, it was determined that a secure computational system is a feasible solution to improve the management of medical prescriptions, would be accepted by most of the patients, and could serve as a basis for future developments in other areas of the healthcare sector. The literature on blockchain in prescription management supports the system’s benefits. A 10% margin of error was used to maintain validity with a smaller sample size.

Blockchain Forensics: A Systematic Literature Review of Techniques, Applications, Challenges, and Future Directions

Blockchain technology has gained significant attention in recent years for its potential to revolutionize various sectors, including finance, supply chain management, and digital forensics. While blockchain’s decentralization enhances security, it complicates the identification and tracking of illegal activities, making it challenging to link blockchain addresses to real-world identities. Also, although immutability protects against tampering, it introduces challenges for forensic investigations as it prevents the modification or deletion of evidence, even if it is fraudulent. Hence, this paper provides a systematic literature review and examination of state-of-the-art studies in blockchain forensics to offer a comprehensive understanding of the topic. This paper provides a comprehensive investigation of the fundamental principles of blockchain forensics, exploring various techniques and applications for conducting digital forensic investigations in blockchain. Based on the selected search strategy, 46 articles (out of 672) were chosen for closer examination. The contributions of these articles were discussed and summarized, highlighting their strengths and limitations. This paper examines the selected papers to identify diverse digital forensic frameworks and methodologies used in blockchain forensics, as well as how blockchain-based forensic solutions have enhanced forensic investigations. In addition, this paper discusses the common applications of blockchain-based forensic frameworks and examines the associated legal and regulatory challenges encountered in conducting a forensic investigation within blockchain systems. Open issues and future research directions of blockchain forensics were also discussed. This paper provides significant value for researchers, digital forensic practitioners, and investigators by providing a comprehensive and up-to-date review of existing research and identifying key challenges and opportunities related to blockchain forensics.

Balancing Data Privacy and Compliance in Blockchain-Based Financial Systems

This study explores the balance between data privacy and regulatory compliance in blockchain-based financial systems, focusing on privacy-enhancing technologies (PETs) such as Zero-Knowledge Proofs (ZKPs) and multiparty computations (MPCs). Through a comprehensive methodology combining literature review, comparative analysis, and empirical testing on the Ethereum test network, the research reveals significant trade-offs. Implementing ZKPs increased transaction times from 5 seconds to 12 seconds and gas fees from 0.02 ETH to 0.05 ETH, while computational load rose by 60%, highlighting the impact on scalability and efficiency. Chi-Square tests and regression analysis uncovered notable algorithmic biases, with low-value accounts experiencing 15% fewer transaction approvals and small mining pools receiving 20% fewer rewards than larger counterparts. Additionally, MPCs, while offering robust privacy, increased communication overhead by 35%, posing scalability challenges. The study recommends adopting a tiered privacy approach, implementing basic privacy measures for low-sensitivity transactions, and advanced technologies like ZKPs for high-sensitivity transactions while optimizing ZKPs to reduce their computational burden and enhance transaction speeds, and integrating artificial intelligence to detect and mitigate algorithmic biases in blockchain systems. Future research should also explore hybrid privacy solutions that combine the strengths of different PETs, such as ZKPs and MPCs, to achieve both robust privacy and high efficiency. Furthermore, investigating quantum-resistant cryptographic methods is crucial to safeguarding blockchain systems against emerging threats. These insights provide valuable guidance for financial institutions, blockchain developers, and policymakers, promoting the development of blockchain-based financial systems that optimize data privacy while maintaining system performance and regulatory compliance.

Bodyless block propagation: TPS fully scalable blockchain with pre-validation

Despite numerous prior attempts to boost transaction per second (TPS) of blockchain system, most of them were at a price of degraded decentralization and security. In this paper, we propose a bodyless block propagation (BBP) scheme for which the blockbody is not validated and transmitted during the block propagation process, to increase TPS without compromising security. Rather, the nodes in the blockchain network anticipate the transactions and their ordering in the next upcoming block so that these transactions can be pre-executed and pre-validated before the birth of the block. It is critical, however, that all nodes have a consensus on the transaction content of the next block.This paper puts forth a transaction selection, ordering, and synchronization algorithm to drive the nodes to reach such a consensus. Yet, the Coinbase Address of the miner of the next block cannot be anticipated, and therefore transactions that depend on the Coinbase Address cannot be pre-executed and pre-validated. This paper further puts forth an algorithm to deal with such unresolvable transactions for an overall consistent and TPS-efficient scheme. With our scheme, most transactions do not need to be validated and transmitted during block propagation, ridding the dependence of propagation time on the number of transactions in the block, and making the system fully TPS scalable. Experimental results show that our protocol can reduce propagation time by 4× with respect to the current Ethereum blockchain, and its TPS performance is limited by the node hardware performance rather than block propagation.

AHEAD: A Novel Technique Combining Anti-Adversarial Hierarchical Ensemble Learning with Multi-Layer Multi-Anomaly Detection for Blockchain Systems

Blockchain technology has impacted various sectors and is transforming them through its decentralized, immutable, transparent, smart contracts (automatically executing digital agreements) and traceable attributes. Due to the adoption of blockchain technology in versatile applications, millions of transactions take place globally. These transactions are no exception to adversarial attacks which include data tampering, double spending, data corruption, Sybil attacks, eclipse attacks, DDoS attacks, P2P network partitioning, delay attacks, selfish mining, bribery, fake transactions, fake wallets or phishing, false advertising, malicious smart contracts, and initial coin offering scams. These adversarial attacks result in operational, financial, and reputational losses. Although numerous studies have proposed different blockchain anomaly detection mechanisms, challenges persist. These include detecting anomalies in just a single layer instead of multiple layers, targeting a single anomaly instead of multiple, not encountering adversarial machine learning attacks (for example, poisoning, evasion, and model extraction attacks), and inadequate handling of complex transactional data. The proposed AHEAD model solves the above problems by providing the following: (i) data aggregation transformation to detect transactional and user anomalies at the data and network layers of the blockchain, respectively, (ii) a Three-Layer Hierarchical Ensemble Learning Model (HELM) incorporating stratified random sampling to add resilience against adversarial attacks, and (iii) an advanced preprocessing technique with hybrid feature selection to handle complex transactional data. The performance analysis of the proposed AHEAD model shows that it achieves higher anti-adversarial resistance and detects multiple anomalies at the data and network layers. A comparison of the proposed AHEAD model with other state-of-the-art models shows that it achieves 98.85% accuracy against anomaly detection on data and network layers targeting transaction and user anomalies, along with 95.97% accuracy against adversarial machine learning attacks, which surpassed other models.

An efficient model of enhanced optimization and dilated-GRU based secured multi-access edge computing with blockchain for VANET sector

The establishment of Vehicular Ad Hoc Networks (VANETs) has brought significant advantages to humans, yet it also raises crucial safety considerations. Security is one of the major challenges in VANETs and is receiving significant attention. Hostile Onboard Units (OBUs) can use a variety of tactics including blocking, monitoring and tampering to harm neighboring OBUs to make illicit profits. The widespread use of the decentralized architecture built around blockchain technology has enabled the clear, safe, and dispersed storage and transmission of VANET application-related information without the need for an administrative center of authority. Perhaps the most important parts of the blockchain-driven VANET system involve implementing an efficient and adaptable consensus system, which remains an open academic issue. The objective of the suggested solution is to create a deep learning model for blockchain that will guarantee VANET security. This network topology is comprised of three layers: “perception, edge computing, and services”. The initial layer that comprises the blockchain operation is employed to demonstrate the privacy of VANET information. Additionally, cloud-based services as well as edge computing are used by the perception layer. The data is safeguarded by the service layer through the application of the blockchain system and storage in the cloud. The last layer aids in meeting user criteria for throughput and QoS. The main objective of this model is to evaluate the reliability of vehicle nodes maintained on the blockchain. Here, the node authentication is handled by an Adaptive Dilated Gated Recurrent Unit (AD-GRU), where the hyper-parameters of the recommended AD-GRU model are optimally tuned by an Enhanced Osprey Optimization Algorithm (EOOA). Finally, the proposed system is simulated and its extensive results are carried out. In contrast with other approaches, the novel system delivers the superior results of securing the data over the VANET environment.

A Trusted Supervision Paradigm for Autonomous Driving Based on Multimodal Data Authentication

At the current stage of autonomous driving, monitoring the behavior of safety stewards (drivers) is crucial to establishing liability in the event of an accident. However, there is currently no method for the quantitative assessment of safety steward behavior that is trusted by multiple stakeholders. In recent years, deep-learning-based methods can automatically detect abnormal behaviors with surveillance video, and blockchain as a decentralized and tamper-resistant distributed ledger technology is very suitable as a tool for providing evidence when determining liability. In this paper, a trusted supervision paradigm for autonomous driving (TSPAD) based on multimodal data authentication is proposed. Specifically, this paradigm consists of a deep learning model for driving abnormal behavior detection based on key frames adaptive selection and a blockchain system for multimodal data on-chaining and certificate storage. First, the deep-learning-based detection model enables the quantification of abnormal driving behavior and the selection of key frames. Second, the key frame selection and image compression coding balance the trade-off between the amount of information and efficiency in multiparty data sharing. Third, the blockchain-based data encryption sharing strategy ensures supervision and mutual trust among the regulatory authority, the logistic platform, and the enterprise in the driving process.

Duo‐H: An Effectual Consensus Algorithm Using Two‐Tier Shard Consortium Blockchain Mechanism for Enhanced Privacy Protection

ABSTRACTBlockchain is an innovative technology for storing data in decentralized, distributed, and secure chain blocks. Consortium blockchains are commonly used in transactions where transactions between organizations are also achieved by the blockchain. In the classic consortium blockchain system, entire nodes are added to each other in the process of transaction consensus. This leads to lower confidentiality in protecting transaction data within the organizations in the consortium. The throughput of the existing consortium blockchain system is still low. To solve the above problems, the paper proposes a two‐tier consortium blockchain with transaction privacy based on sharding technology. First, a trust value assessment is carried out to select the nodes of the blockchain. The duo‐head observation strategy uses these trust values to identify the nonmalicious node. Finally, the consensus separation approach based on the guarantee mechanism strategy with the shard nodes is presented. This approach is used to select reliable nodes for the blocks to be stored. The proposed consortium blockchain approach evaluation is done in terms of latency, throughput, and transactions per second metrics. As a result of the evaluations, the proposed model with 32 shards possesses 143,891 of throughput and 1.11 s of latency. Moreover, by the proposed two‐tier consortium model, time consumption is also decreased when uploading data. For a data set of 50,000, the suggested model has a time consumption of 96 s. The proposed research results in higher throughput and less latency in transactions. Also, the research enhances the scalability and reliability by overcoming the poor node issues.

UtilityChain: Dynamic Resource Allocation for Mining and Servicing in Blockchain System

<i>UtilityChain:</i> Dynamic Resource Allocation for Mining and Servicing in Blockchain System

Lattice-Like Blockchain System for Maintaining the Data Privacy in the Distributed Computation of Carbon Emission

Lattice-Like Blockchain System for Maintaining the Data Privacy in the Distributed Computation of Carbon Emission

Identifying patterns and mechanisms of AI integration in blockchain for e-voting network security

The study focuses on the enhancement of e-voting blockchain network security through the integration of artificial intelligence. The critical problem addressed is the existing limitations in real-time threat detection and anomaly detection within blockchain transactions. These limitations can compromise the integrity and security of blockchain networks, making them vulnerable to attacks and fraudulent activities. The core results of the research include the development and implementation of sophisticated AI algorithms designed to enhance the monitoring of blockchain transactions and the auditing of smart contracts. These AI-driven advancements introduce unique features, such as the capability to detect and respond to security threats and anomalies in real-time. This significantly strengthens and optimizes the security frameworks of blockchain systems in e-voting. These results are explained by the strategic application of machine learning and natural language processing methodologies. By employing these advanced AI techniques, the study has achieved more accurate and efficient threat detection, thereby addressing the security challenges previously mentioned. The practical applications of these findings are extensive and diverse. Enhanced security mechanisms can be utilized in financial transactions, supply chain management, and decentralized applications, providing a robust framework for improved blockchain-based e-voting security. In conclusion, integrating AI into blockchain security mechanisms addresses current limitations in threat detection and offers a scalable and effective solution for future security challenges

Blockchain-based proxy re-encryption access control method for biological risk privacy protection of agricultural products

In today’s globalized agricultural system, information leakage of agricultural biological risk factors can lead to business risks and public panic, jeopardizing corporate reputation. To solve the above problems, this study constructs a blockchain network for agricultural product biological risk traceability based on agricultural product biological risk factor data to achieve traceability of biological risk traceability data of agricultural product supply chain to meet the sustainability challenges. To guarantee the secure and flexible sharing of agricultural product biological risk privacy information and limit the scope of privacy information dissemination, the blockchain-based proxy re-encryption access control method (BBPR-AC) is designed. Aiming at the problems of proxy re-encryption technology, such as the third-party agent being prone to evil, the authorization judgment being cumbersome, and the authorization process not automated, we design the proxy re-encryption access control mechanism based on the traceability of agricultural products’ biological risk factors. Designing an attribute-based access control (ABAC) mechanism based on the traceability blockchain for agricultural products involves defining the attributes of each link in the agricultural supply chain, formulating policies, and evaluating and executing these policies, deployed in the blockchain system in the form of smart contracts. This approach achieves decentralization of authorization and automation of authority judgment. By analyzing the data characteristics within the agricultural product supply chain to avoid the malicious behavior of third-party agents, the decentralized blockchain system acts as a trusted third-party agent, and the proxy re-encryption is combined with symmetric encryption to improve the encryption efficiency. This ensures a efficient encryption process, making the system safe, transparent, and efficient. Finally, a prototype blockchain system for traceability of agricultural biological risk factors is built based on Hyperledger Fabric to verify this research method’s reliability, security, and efficiency. The experimental results show that this research scheme’s initial encryption, re-encryption, and decryption sessions exhibit lower computational overheads than traditional encryption methods. When the number of policies and the number of requests in the access control session is 100, the policy query latency is less than 400 ms, the request-response latency is slightly more than 360ms, and the data uploading throughput is 48.7 tx/s. The data query throughput is 81.8 tx/s, the system performance consumption is low and can meet the biological risk privacy protection needs of the agricultural supply chain. The BBPR-AC method proposed in this study provides ideas for achieving refined traceability management in the agricultural supply chain and promoting digital transformation in the agricultural industry.

DBUP: Dynamic blockchain UTXO processing for storage efficiency optimization

In traditional blockchain systems, the Unsent Transaction Output (UTXO) dataset stores all generated UTXOs, which typically represent digital assets of the users. UTXOs can be used to track the output status of cryptocurrency transactions. However, with the development of blockchain networks, the UTXO dataset continuously increasing in scale. In the UTXO dataset, a large number of low-value UTXOs occupied huge storage space, making the blockchain dataset expanded. This impacts the running performance of the whole blockchain system. To address this issue, this paper proposes the Dynamic Blockchain UTXO Processing strategy (DBUP) for UTXO storage efficiency optimization. This strategy aims to rapidly consume the low-value UTXOs, effectively alleviating UTXO dataset expansion. Furthermore, Through improved simulated annealing (ISA) algorithm and a UTXO dynamic adjustment mechanism in DBUP, the strategy ensures that the transaction fee in the transaction process remain at a low level. The experimental evaluation indicates that our strategy can achieve the better performance against the Low Value First (LVF) and the First-In-First-Out (FIFO) strategies, enhancing the storage efficiency of blockchain systems.

Serein: A parallel pipeline‐based DAG schema for consensus in blockchain

AbstractAs the core technology of blockchain, consensus mechanisms play a crucial role in ensuring the consistency and reliability of blockchain systems. In a decentralized and open system environment like blockchain, traditional consensus algorithms are often unsuitable due to their inability to tolerate arbitrary faults such as malicious node behaviour. Consequently, Byzantine fault tolerance consensus algorithms have become a focal point in blockchain systems. However, as Byzantine fault tolerance consensus algorithms have evolved, they still face significant challenges, particularly in addressing issues related to network latency and throughput. This paper proposes a parallel pipeline‐based DAG schema for consensus in blockchain, Serein. Firstly, the Serein algorithm achieves functional partitioning of nodes, enhancing their scalability. Secondly, it employs a pipeline structure, allowing each block to proceed without waiting for the previous block's result, thereby reducing block generation latency. Lastly, the Serein algorithm leverages the advantages of the DAG block structure to achieve concurrent block ordering and submission, improving system throughput. Experimental results demonstrate that the proposed Serein algorithm maintains robust performance under conditions of high transaction volume with multiple nodes, effectively enhancing consensus efficiency while ensuring Byzantine fault‐tolerant security.

A digital resource copyright protection scheme based on blockchain cross-chain technology

With the expansion of the digital publishing industry, copyright infringement of digital resources has become increasingly rampant. Blockchain technology offers a promising solution for copyright protection of digital resources. However, existing copyright protection methods based on single-chain or consortium blockchains have disadvantages such as low system processing efficiency, poor scalability, and a lack of alignment with real-world business needs. To address the current challenges of blockchain technology in the field of copyright protection and establish a more effective solution for protecting digital resource copyrights, while also expanding the application of blockchain cross-chain technology and promoting its development, leveraging blockchain cross-chain theory, technology, and methods, a digital resource copyright protection scheme based on blockchain cross-chain technology was constructed. It outlines detailed processes for copyright registration, resource transactions, copyright modifications, and copyright enforcement. Furthermore, this copyright protection scheme was analyzed. The findings indicated that the blockchain cross-chain technology is suitable for digital resource copyright protection. The proposed copyright protection scheme can allow various stakeholders to better fulfill their roles, in addition to enhancing the overall maintainability and scalability of the blockchain system.

Carbon monitoring, reporting and verification (MRV) for cleaner built environment: Developing a solar photovoltaic blockchain tool and applications in Hong Kong's building sector

Photovoltaic (PV) systems offer significant potential for net-zero emission targets, due to their flexibility and high exploitation capacity. However, effective carbon monitoring, reporting, and verification (MRV) methods are crucial for their life cycle. The present study introduces a novel blockchain-based carbon auditing tool aimed at supporting the life cycle assessment of PV systems in Hong Kong's building sector. Consequently, all relevant information throughout the PV system's life cycle is uploaded and securely recorded within the blockchain system. The decentralized network structure, distributed consensus, and cryptographic methods employed by blockchain technology ensure the traceability, immutability, and transparency of the recorded information. Furthermore, this study provides an analysis of the installation potential and life cycle environmental benefits of PV systems in Hong Kong's building sector. Finally, the feasibility of the proposed framework is demonstrated through case studies conducted in Hong Kong. These findings underline the reliability of the proposed blockchain-based tool as a technological foundation for carbon MRV in the Hong Kong building PV sector.

Zebra: A cluster-aware blockchain consensus algorithm

The Consensus algorithm is the core of the permissioned blockchain, it directly affects the performance and scalability of the system. Performance is limited by the computing power and network bandwidth of a single leader node. Most existing blockchain systems adopt mesh or star topology. Blockchain performance decreases rapidly as the number of nodes increases. To solve this problem, we first design the n-k cluster tree and corresponding generation algorithm, which supports rapid reconfiguration of nodes. Then we propose the Zebra consensus algorithm, which is a cluster tree-based consensus algorithm. Compared to the PBFT, it has higher throughput and supports more nodes under the same hardware conditions. However, the tree network topology enhances scalability while also increasing latency among nodes. To reduce transaction latency, we designed the Pipeline-Zebra consensus algorithm that further improves the performance of blockchain systems in a tree network topology through parallel message propagation and block validation. The message complexity of the algorithm is O(n). Experimental results show that the performance of the algorithm proposed in this paper can reach 2.25 times that of the PBFT algorithm, and it supports four times the number of nodes under the same hardware.