IET Blockchain最新文献

With the rapid application of consortium chains, supervising these systems has become a challenge for governments. The centralized model fails to deliver supervision services that are both open and transparent. Given the benefits of decentralization, non-tampering, and traceability offered by blockchains, researchers propose the concept of ‘governing the chain by chain’, which involves supervising multiple consortium chains by constructing a blockchain. Under this idea, the cross-chain scheme becomes the key to achieving excellent supervision. Existing studies have shortcomings and cannot meet the requirements of universality, security, and efficiency in cross-chain supervision scenarios. Aiming at the challenges, we propose ChainKeeper, a cross-chain scheme for governing the chain by chain. The innovation of our work lies in three points. First, a modular node proxy program is designed to adapt to various implementations of consortium chains. Second, a verifiable node random selection method is put forward to improve the throughput of cross-chain data transmission. Finally, a verifiable identity threshold signature method is proposed to prevent the cheating behavior of malicious nodes. To verify the universality of ChainKeeper, we built a prototype system on three types of consortium chains. The experimental results show that ChainKeeper can achieve high throughput, outperforming two state-of-the-art cross-chain schemes.

The security of smart contract has always been one of the significant problems in blockchain. As shown in previous studies, vulnerabilities in smart contracts can lead to unpredictable losses. With the rapid growth of the number of smart contracts, more and more data driven detection technologies based on machine learning have been proposed. However, some state-of-the-art approaches mainly rely on the source code of smart contract. These methods are limited by the openness of the source code and the version of the programming language. To address this problem, we propose a novel vulnerability detection method based on transformer by constructing the control flow graph (CFG) of smart contracts operation codes (opcodes), which shields the difference of various versions of program language. Extensive experiments are conducted to evaluate the effectiveness of the proposed method on the authors' own collected dataset. The experimental results show that the proposed method achieves 94.36% accuracy in vulnerability detection, which performs better than other state-of-the-art methods.

The anonymous and tamper-proof nature of the blockchain poses significant challenges in auditing and regulating the behaviour and data on the chain. Criminal activities and anomalies are frequently changing, and fraudsters are devising new ways to evade detection. Moreover, the high volume and complexity of transactions and asymmetric errors make data classification more challenging. Also, class imbalances and high labelling costs are hindering the development of effective algorithms. In response to these issues, the authors present BlockDetective, a novel framework based on GCN that utilizes student–teacher architecture to detect fraudulent cryptocurrency transactions that are related to money laundering. The authors’ method leverages pre-training and fine-tuning, allowing the pre-trained model (teacher) to adapt better to the new data distribution and enhance the prediction performance while teaching a new, light-weight model (student) that provides abstract and top-level information. The authors’ experimental results show that BlockDetective outperforms state-of-the-art research methods by achieving top-notch performance in detecting fraudulent transactions on the blockchain. This framework can assist regulators and auditors in detecting and preventing fraudulent activities on the blockchain, thereby promoting a more secure and transparent financial system.

The reentrancy vulnerability in smart contracts has caused significant losses in the digital currency economy. Existing solutions for detecting and repairing this vulnerability are limited in scope and lack a comprehensive framework. Additionally, there is currently a lack of guidance methods for effectively pinpointing the location of vulnerabilities. The proposed bytecode-level method addresses these challenges by incorporating a detection module, an auxiliary localization module, and a repair module. An opcode classification method is introduced using vulnerability features and a BiLSTM-Attention-based sequence model to enhance detection accuracy. To overcome difficulties in vulnerability localization, an auxiliary localization method based on data flow and control flow analysis is proposed, enabling developers to better locate vulnerabilities. Current reentrancy vulnerability repair methods are analyzed and strategies for three reachable patterns are proposed. The bytecode rewriting strategy utilizes Trampoline technology for repair, while a fuel optimization method reduces bytecode generation length to optimize gas costs. Through extensive experimental validation, the effectiveness and superiority of the proposed methods are confirmed, further validating the feasibility of the entire framework. Experimental results demonstrate that the framework offers enhanced protection against reentrancy vulnerability attacks in smart contracts.

A $��(�t�,�m�)�$-threshold secret sharing and multisecret-sharing scheme based on Shamir's secret sharing scheme are introduced with two-level security using a one-way function. Besides, its application in a smart contract-enabled consortium blockchain network is discussed. The proposed scheme is thoroughly examined in terms of security and efficiency. Privacy, security, integrity, and scalability are also analyzed while applying it to the blockchain network.

At present, cross-chain technology and cross-chain system pay more attention to cross-chain security and performance, and have shown great improvement in security, scalability and efficiency. However, the generality of cross-chain technology in different scenarios is poor, and it may not fit in different business fields. In order to solve the generality problem of cross-chain technology, this paper sorts out and analyzes the research of blockchain cross-chain protocol, and proposes a fusion protocol cross-chain method based on relay-chain technology. This paper will introduce the cross-chain framework and cross-chain transaction process of fusion protocol. Cross-chain framework includes blockchain cross-chain interaction architecture, cross-chain interaction governance mechanism and cross-chain privacy protection scheme. The transaction process is divided into three different stages. Finally, combined with cross-chain key technologies and landing application requirements, the future research direction of blockchain cross-chain technology is given. The fusion protocol cross-chain method based on relay-chain technology proposed here is a groundbreaking innovation in the field of blockchain cross-chain technology. By integrating the advantages of different blockchain systems and protocols, the fusion protocol is able to achieve a high degree of generality and compatibility, making it suitable for various business scenarios and applications.

Cryptocurrencies, particularly Bitcoin, have garnered attention for their potential in anonymous transactions. However, their anonymity has often been compromised by deanonymization attacks. To counter this, mixing services have been introduced. While they enhance privacy, they obscure fund traceability. This study seeks to demystify transactions linked to these services, shedding light on pathways of concealed and laundered money. We propose a method to identify and classify transactions and addresses of major mixing services in Bitcoin. Unlike previous research focusing on older techniques like CoinJoin, we emphasize modern mixing services. We gathered labelled data by transacting with three prominent mixers (MixTum, Blemder, and CryptoMixer) and identified recurring patterns. Using these patterns, an algorithm was created to pinpoint mixing transactions and distinguish mixer-related addresses.

The algorithm achieved a remarkable recall rate of 100%. Given the lack of clear ground truth and the vast number of unlabelled transactions, ensuring accuracy was a challenge. However, by analyzing a set of non-mixing transactions with our model, it was confirmed that the high recall rate was not misleading. This work provides a significant advancement in monitoring mixing transactions, presenting a valuable tool against fraud and money laundering in cryptocurrency networks.

Blockchain is considered to be a promising technology due to its decentralization, open, and non tamperation. Nevertheless, the current blockchain system cannot replace traditional financial institutions because its performance and security indicators in the consensus algorithm are not perfect. As one of the emerging capacity expansion technologies, sharding technology improves throughput by grouping users and processing transactions in parallel. This paper proposes an improved Byzantine Fault Tolerance algorithm TV-BRAFT. TV-BRAFT calculates a trust value for each user through an incentive mechanism. The sharding scheme based on the trust value is adopted to reduce the probability of malicious nodes being allocated to the same partition, thus improving the security of the system. In the consensus, the system selects users with qualified trust values to form a verification group to supervise the consensus process and avoid submitting error transactions to the blockchain. In order to prove that the TV-BRAFT can resist Byzantine attacks, a recursive formula is used to prove the security of sharding and the security of consensus within one shard. Finally, the simulation results show that the TV-BRAFT not only guarantees the fairness of incentives but is also better than the Raft and PBFT algorithms in efficiency and throughput.