An Efficient Code-Embedding-Based Vulnerability Detection Model for Ethereum Smart Contracts

【Author】 Xu, Zhigang; Chen, Xingxing; Dong, Xinhua; Han, Hongmu; Yan, Zhongzhen; Ye, Kangze; Li, Chaojun; Zheng, Zhiqiang; Wang, Haitao; Zhang, Jiaxi

【Source】INTERNATIONAL JOURNAL OF DATA WAREHOUSING AND MINING

【影响因子】1.333

【Abstract】Efficient and convenient vulnerability detection for smart contracts is a key issue in the field of smart contracts. The earlier vulnerability detection for smart contracts mainly relies on static symbol analysis, which has high accuracy but low efficiency and is prone to path explosion. In this paper, the authors propose a static method for vulnerability detection based on deep learning. It first disassembles Ethereum smart contracts into opcode sequences and then converts the vulnerability detection problem into a natural language text classification problem. The word vector method is employed to map each opcode to a uniform vector space, and the opcode sequence matrix is trained by the TextCNN method to detect vulnerabilities. Furthermore, a code obfuscation method is given to enhance and balance the dataset, while three different opcode sequence generation methods are proposed to construct features. The experimental results verify that the average prediction accuracy of each smart contract exceeds 96%, and the average detection time is less than 0.1 s.

【Keywords】Disassembly; Embedding; Ethereum; Machine Learning; Natural Language Processing; Smart Contracts; Static Method; Vulnerability Detection

【发表时间】2023 APR-JUN

【收录时间】2023-08-23

【文献类型】实证数据

【主题类别】

区块链治理-技术治理-智能合约漏洞检测