Research on Smart Contract Vulnerability Detection Method Based on Improved Graph Neural Network

Abstract

Aiming at the problem that smart contract security vulnerability detection faces high false positives in static analysis and low efficiency in dynamic analysis, which leads to low accuracy of vulnerability detection, a smart contract vulnerability detection method based on improved graph neural network (EGN, Event-Enhanced GNN) was proposed. Firstly, security mode features and graph features were extracted. Security mode features reduced false positives caused by blind detection, and graph features avoided the performance bottleneck of full graph traversal. Secondly, the high-risk functions were screened based on the risk probability threshold to improve the overall analysis efficiency. Thirdly, the temporal graph neural network was deployed for high-risk functions, the event temporal graph was dynamically tracked and the self-attention mechanism was used to capture vulnerabilities, so as to enhance the detection ability of complex vulnerabilities. Finally, we focus on reentrant vulnerability and timestamp dependency vulnerability detection. Through the evaluation experiments on the real contract datasets of two platforms of Ethereum and VNT chain, the experimental results show that the accuracy and F1 value of the proposed model for detecting reentries vulnerability reach 93.12% and 94.29% respectively, and the accuracy and F1 value of timestamp dependency vulnerability reach 91.71% and 91.42% respectively, which are better than the existing methods.