IET Blockchain最新文献

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.

The application of Blockchain and augmented technologies such as IoT, AI, and Big Data platforms present a feasible approach for resolving the implementation challenges of trusted, decentralized platforms. This article proposes a DevOps framework for the specification of Blockchain use-cases that enables evaluation, replication, and benchmarking. Specifically, it could be applied to specify the requirements and design characteristics of Blockchain applications in terms of key attributes such as: (i) transparency; (ii) traceability; (iii) tamper-resistance; (iv) immutability; and (v) compliance. The article first introduces the design characteristics of Blockchain as a Platform and then examines successful use-cases for its implementation using the above attributes. It may be conjectured that the 3TIC framework would serve as the basis of a cross industry process for Blockchain. The intended contribution is that such a standard process will support industry-academia collaboration in the development of Blockchain platforms and services of relevance and utility as it can be applied by firms to structure their requirements and design specifications.

Implementing effective regulation of blockchain transactions has become a research hotspot in recent years. However, most of the current regulatory schemes are customized for specific blockchain applications and lack versatility and scalability. Meanwhile, these schemes cannot guarantee fairness due to the non-disclosure of regulatory policies and regulatory processes. To address these issues, a scalable policy-based regulatory architecture (SPRA) is proposed for blockchain transactions that separates regulation and application to provide sufficient scalability. SPRA is a four-layer model (permission layer, regulation layer, bridge layer, and business layer). A regulatory policy description language (XRPL) is designed to define the regulatory rules and specifications for interoperability between the layers. A decentralized jury mechanism (JuryBC) based on the Shamir threshold secret sharing algorithm and Pedersen commitment is proposed at the regulation layer to avoid regulatory arbitrariness and unfairness. We also construct a secure and efficient regulatory data sharing scheme (RDShare) at the business layer using an attribute-based encryption algorithm. The key parameters in both JuryBC and RDShare can be specified in the regulatory policy to suit different application scenarios. Finally, the security of the architecture is analyzed and the feasibility and scalability of the architecture through simulation experiments are demonstrated.

As of today, blockchain technology is virtually absent from rail transit applications, however, it has promising potential. For cloud-based systems or conventional systems, there is too much information at the centre, resulting in a high level of data processing. However, if the data processing capability can be provided at the edge station or at the edge equipment, it greatly reduces the operation and maintenance pressure, at the same time, if the edge device takes the form of a blockchain, it is possible to increase the safety protection capability. It is important to reduce operating and maintenance risks for metro equipment systems by installing fault diagnosis and health management capabilities. This paper presents a blockchain-based platform for subway applications. The assistance includes design, construction, operation, and maintenance assistance, energy saving assistance, manpower reduction assistance, fire protection linkage assistance at transfer stations, federal learning analysis and artificial intelligence analysis, as well as specific implementation plans for implementing vent valve equipment as examples.

The use of federated learning to achieve blockchain interoperability has become a hot topic in research, because it enables data exchange without revealing any private information. However, the previous work, such as ScaleSFL (Asia-CCS, 2022), that has implemented federated learning for blockchain interoperability, the throughput of the framework still cannot support the practical applications. Therefore, a federated learning framework based on Directed Acyclic Graph (DAG) is proposed which utilizes sharding mechanism to enhance the blockchain interoperability. By constructing a weighted context graph based on data correlation, reasonable sharding of the dataset is achieved, thereby improving the efficiency of blockchain interoperability. The experimental results show that the federated framework reduces global computation in federated learning by 30% compared with other schemes, while increasing blockchain throughput by nearly 40%.

With the development of blockchain, cryptocurrencies are also showing a boom. However, due to the decentralized and anonymous nature of blockchain, cryptocurrencies have inevitably become a hotbed for fraudulent crimes. For example, phishing scams are frequent, which not only jeopardize the financial security of blockchain, but also hinder the promotion of blockchain technology. To solve this problem, this paper proposes a graph neural network-based phishing detection method for Ethereum, and validates it using Ethereum datasets. Specifically, this paper proposes a feature learning algorithm named TransWalk, which consists of a random walk strategy for transaction networks and a multi-scale feature extraction method for Ethereum. Then, an Ethereum phishing fraud detection framework is built based on TransWalk, and conduct extensive experiments on the Ethereum dataset to verify the effectiveness of this scheme in identifying Ethereum phishing detection.

Along with the continuous breakthroughs and innovations in blockchain technology, various blockchain application scenarios are emerging, such as healthcare, e-voting, and Internet of Things. However, in general, these applications need to be supported by blockchain systems with different characteristics. Different blockchain systems have significant differences in architecture, security, efficiency, and so on, which makes the exchange of information between applications have certain barriers. Therefore, the research of blockchain cross-chain technology brings hope for breaking the information barriers between blockchain systems. This paper describes the basic principles and latest research of three mainstream cross-chain technologies; then analyzes three innovative cross-chain solutions of cross-chain routing, smart contracts, and multi-chain consensus; furthermore, discusses the application and security risks of cross-chain technologies, and finally summarizes and outlooks on cross-chain technologies.

Over the past few years, blockchain platforms supporting WebAssembly (Wasm) smart contracts are gaining popularity. However, Wasm smart contracts are often compiled from memory-unsafe languages (e.g. C and C++). And there is a lack of effective defense against integer overflow and stack overflow at the compiler and virtual machine (VM) layers, making Wasm smart contracts even more exploitable than native C and C++ programs. In this paper, the authors propose wasm overflow detector (WASMOD) to address the integer overflow and stack overflow vulnerabilities. The authors’ approach combines bytecode instrumentation, run-time validation, and grey-box fuzzing to detect these vulnerabilities. The authors applied their approach to the popular EOSIO blockchain and evaluated it on 4616 deployed Wasm smart contracts. The authors’ approach detected 13 real-world vulnerable smart contracts.

The Byzantine Fault Tolerant (BFT) consensus algorithms have been widely applied in the blockchain systems because of their fault tolerance capability to determine system consistency in the presence of malicious nodes. However, the BFT consensus algorithms are confronted with low efficiency and scalability problems caused by multiple rounds of handshake communication. In this paper, a pipeline-based Fast Pipeline Byzantine Fault Tolerance consensus algorithm (FP-BFT) is proposed, which adopts a non-leader pipeline framework to process different rounds of transactions in parallel. By means of randomly selecting 2f+1 nodes to form a committee for one round of transactions, consensus agreement can be reached within the committee via nodes broadcasting and voting. Committee nodes participating in the consensus are chosen by chance to avoid the monopoly of which becomes the block producer. Consensus efficiency and the system throughput can be significantly improved with the pipeline framework. Comparison experiments are conducted to verify the superiority of the FP-BFT algorithm, and the theoretical proof is given to guarantee the Byzantine fault-tolerant security. Experimental results show that FP-BFT has improved the consensus efficiency by decreasing communication overhead to make it better applied both in public blockchain and consortium blockchain systems.