IET Blockchain最新文献

With the rapid development of blockchain technology in the financial sector, the security of blockchain is being put to the test due to an increase in phishing fraud. Therefore, it is essential to study more effective measures and better solutions. Graph models have been proven to provide abundant information for downstream assignments. In this study, a graph-based embedding classification method is proposed for phishing detection on Ethereum by modeling its transaction records using subgraphs. Initially, the transaction data of normal addresses and an equal number of confirmed phishing addresses are collected through web crawling. Multiple subgraphs using the collected transaction records are constructed, with each subgraph containing a target address and its nearby transaction network. To extract features of the addresses, a modified Graph2Vec model called imgraph2vec is designed, which considers block height, timestamp, and amount of transactions. Finally, the Extreme Gradient Boosting (XGBoost) algorithm is employed to detect phishing and normal addresses. The experimental results show that the proposed method achieves good performance in phishing detection, indicating the effectiveness of imgraph2vec in feature acquisition of transaction networks compared to existing models.

A major drawback in deploying central bank digital currencies (CBDC) is the offline puzzle, which requires that a CBDC must keep the liquidity provision given by cash, and, simultaneously, avoid double-spending, cloning, and other issues. The puzzle is solved by minting the coins in serial numbers, which are stored on a local blockchain inside a smartphone or EMV card. The local blockchain is strengthened by a two-stage approval architecture that mitigates attacks and enables non-repudiation handling. The coins are protected by hardware keys embedded in the microchip and can be continuously mined by the wallet to enhance security. The coins can be either minted as hot coins, which can be retrieved in case of loss, or minted as cold coins, like physical cash.

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.

Metaverse is a digital value interaction network based on blockchain technology, with an important economic system component. While both traditional financial industries and crypto-native industries have made significant progress by leveraging blockchain, the value stream of each remains limited to separate ecosystems. To bridge this gap between off-chain and on-chain economic systems, an on-chain trading model was proposed using HD key derivation technique for direct uploading onto chains without going through centralized services for IoT data transmission. To improve the current status of NFTs as static assets, a token protocol binding each NFT with a unique account address was proposed. Additionally, oracle technique was leveraged with a decentralized and distributed trust model spanning across on-chain and off-chain components which securely pushes data between smart contracts and Web-APIs. A decentralized trading model was developed based on smart contracts implementing automated market makers according to CFMM algorithm. Parallel transaction computing was executed based on the DAG model to ensure high operational performance and security standards of underlying blockchain. Finally, the on-chain trading system of real world asset backed digital assets was developed integrating all the above key techniques that correspond to crucial functions of a complete economic system in Metaverse.

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.

In a networked microgrid system (NMS), various heterogeneous microgrids are interconnected. A networked microgrid system facilitates a new kind of physical design that provides numerous advantages such as distributed economic optimization, reliability, resiliency, and focusing on distributed generations and customers. Designing the secure and privacy-protected smart power contract between electricity suppliers and consumers, considered as agents, of different microgrids, is a challenging task in the networked- microgrid system. Each microgrid implements a heterogeneous or isomorphic blockchain based platform. The blockchain interoperability, inherently, presents in different blockchains implemented by various microgrids. This paper reviews the interoperability issues and smart contract designs in blockchain-based systems and proposes new mechanisms to cater blockchain interoperability challenges to facilitate the design of secure and seamless smart contracts among different blockchains of microgrids. A network hub of heterogeneous blockchains of network microgrids has been proposed. A methodology has been developed to transfer tokens between interoperable blockchains. A distributed identity-based microgrid (DIBM) scheme is incorporated to make the networked microgrid system secure and trustworthy. This paper suggests an effective consensus protocol for cross-chain architecture that improves the tokenization system and smart power contract designs. Asynchronous blockchain based federated learning for peer-to-peer smart power exchange has been implemented in learning process of interoperable and heterogeneous blockchain based network hub of microgrid. For simulation purposes, MATLAB and python programming have been used with real-time data of microgrids.

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.