Smart Contract Vulnerability Detection Based on Automated Feature Extraction and Feature Interaction

计算机科学 特征提取 脆弱性(计算) 人工智能 特征(语言学) 特征向量 数据挖掘 脆弱性评估 机器学习 支持向量机 模式识别(心理学) 计算机安全 心理学 哲学 语言学 心理弹性 心理治疗师
作者
Lina Li,Yang Liu,Guodong Sun,Nianfeng Li
出处
期刊:IEEE Transactions on Knowledge and Data Engineering [IEEE Computer Society]
卷期号:: 1-14
标识
DOI:10.1109/tkde.2023.3333371
摘要

Smart contract is the core of blockchain operation, and contract vulnerability will cause huge economic losses. Therefore, effective smart contract vulnerability detection is of vital importance and attracts more and more attention. In this paper, we propose a vulnerability detection model (VDM-AEI) based on automatic feature extraction and feature interaction. For the first time, this model converts smart contracts into gray images and uses VGG16 and GRU models to automatically extract vulnerability features and filter effective features, respectively. Then, a contract graph and an expert knowledge feature vector are constructed by using commonly used methods as part of feature construction. Next, AutoInt and DCN networks are used to build a dual feature interaction network to obtain more abundant vulnerability feature information, which extracts high-dimensional nonlinear features from the low and sparse features of the contract graph feature vector and the expert knowledge-defined feature vector. Finally, all ouput features of GRU, AutoInt and DCN networks are integrated to obtain vulnerability classification results through fully connected neural networks. We conducted extensive experiments on the ESC and VSC datasets for reentrancy vulnerabilities, timestamp dependency vulnerabilities, and infinite loop vulnerabilities. The experimental results prove the effectiveness and accuracy of the VDM-AEI model. Compared with the latest vulnerability detection model CGE, the accuracy rates of the 3 types of vulnerability detection are improved by 10.85%, 6.18%, and 12.34%, respectively. In addition, the predicted F1 scores of VDM-AEI are all greater than 95%, and the recall rate is no less than 94%.
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