Enhancing Smart-Contract Security through Machine Learning: A Survey of Approaches and Techniques

【Author】 Jiang, Fan; Chao, Kailin; Xiao, Jianmao; Liu, Qinghua; Gu, Keyang; Wu, Junyi; Cao, Yuanlong

【Source】ELECTRONICS

【影响因子】2.690

【Abstract】As blockchain technology continues to advance, smart contracts, a core component, have increasingly garnered widespread attention. Nevertheless, security concerns associated with smart contracts have become more prominent. Although machine-learning techniques have demonstrated potential in the field of smart-contract security detection, there is still a lack of comprehensive review studies. To address this research gap, this paper innovatively presents a comprehensive investigation of smart-contract vulnerability detection based on machine learning. First, we elucidate common types of smart-contract vulnerabilities and the background of formalized vulnerability detection tools. Subsequently, we conduct an in-depth study and analysis of machine-learning techniques. Next, we collect, screen, and comparatively analyze existing machine-learning-based smart-contract vulnerability detection tools. Finally, we summarize the findings and offer feasible insights into this domain.

【Keywords】machine learning; safety; smart contract; vulnerability detection; survey

【发表时间】2023 28-Apr

【收录时间】2023-06-03

【文献类型】综述

【主题类别】

区块链技术-核心技术-智能合约