Yaqiong He, Jinlin Fan^*, Huaiguang Wu

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 995-1032, 2024, DOI:10.32604/cmc.2024.052887

Abstract With the rise of blockchain technology, the security issues of smart contracts have become increasingly critical. Despite the availability of numerous smart contract vulnerability detection tools, many face challenges such as slow updates, usability issues, and limited installation methods. These challenges hinder the adoption and practicality of these tools. This paper examines smart contract vulnerability detection tools from 2016 to 2023, sourced from the Web of Science (WOS) and Google Scholar. By systematically collecting, screening, and synthesizing relevant research, 38 open-source tools that provide installation methods were selected for further investigation. From a developer’s perspective,… More >

Zhenxiang He^*, Zhenyu Zhao, Ke Chen, Yanlin Liu

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3023-3045, 2024, DOI:10.32604/cmc.2024.050281

Abstract The fast-paced development of blockchain technology is evident. Yet, the security concerns of smart contracts represent a significant challenge to the stability and dependability of the entire blockchain ecosystem. Conventional smart contract vulnerability detection primarily relies on static analysis tools, which are less efficient and accurate. Although deep learning methods have improved detection efficiency, they are unable to fully utilize the static relationships within contracts. Therefore, we have adopted the advantages of the above two methods, combining feature extraction mode of tools with deep learning techniques. Firstly, we have constructed corresponding feature extraction mode for… More >

Peng Gong^1,2,3, Wenzhong Yang^2,3,*, Liejun Wang^2,3, Fuyuan Wei^2,3, KeZiErBieKe HaiLaTi^2,3, Yuanyuan Liao^2,3

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1439-1462, 2023, DOI:10.32604/cmc.2023.038878

Abstract Smart contracts have led to more efficient development in finance and healthcare, but vulnerabilities in contracts pose high risks to their future applications. The current vulnerability detection methods for contracts are either based on fixed expert rules, which are inefficient, or rely on simplistic deep learning techniques that do not fully leverage contract semantic information. Therefore, there is ample room for improvement in terms of detection precision. To solve these problems, this paper proposes a vulnerability detector based on deep learning techniques, graph representation, and Transformer, called GRATDet. The method first performs swapping, insertion, and symbolization… More >

Qiuyun Lyu¹, Chenhao Ma¹, Yanzhao Shen², Shaopeng Jiao³, Yipeng Sun¹, Liqin Hu^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1625-1647, 2023, DOI:10.32604/cmes.2022.021562

Abstract Smart contracts running on public blockchains are permissionless and decentralized, attracting both developers and malicious participants. Ethereum, the world’s largest decentralized application platform on which more than 40 million smart contracts are running, is frequently challenged by smart contract vulnerabilities. What’s worse, since the homogeneity of a wide range of smart contracts and the increase in inter-contract dependencies, a vulnerability in a certain smart contract could affect a large number of other contracts in Ethereum. However, little is known about how vulnerable contracts affect other on-chain contracts and which contracts can be affected. Thus, we… More >