Abstract: Aiming at the problem of low accuracy caused by high false positives in static analysis and low efficiency in dynamic analysis in smart contract security vulnerability detection,a smart contract vulnerability detection method based on fusion of temporal-feature graph neural network(FTF-GNN) is proposed.Firstly,pattern-based features P_r and graph-based features G_r are extracted through static analysis,which pattern-based features P_r reduce false positives caused by blind detection and graph-based features G_r avoid the bottleneck of whole graph traversal.Secondly,the high-risk functions are screened based on the risk probability threshold λ to improve the overall analysis efficiency.Thirdly,a temporal graph neural network is constructed to enhance the ability to capture complex vulnerabilities through the self-attention mechanism.Finally,it focuses on reentrant vulnerability and timestamp dependency vulnerability detection.Experiments on the real contract datasets of Ethereum and VNT chain show that the accuracy of the proposed model for reentry vulnerability detection reaches 93.12% and the F1 value reaches 94.41%,which are 0.31 percentage points and 2.73 percentage points higher than the best of the existing comparison methods.The ac-curacy of detecting timestamp dependence vulnerability reaches 91.71% and the F1 value reaches 92.47%.Compared with the existing comparison methods,the best ones increase by 0.07 percentage points and 1.10 percentage points.

Key words: Blockchain, Smart contract, Vulnerability detection, Temporal feature, Graph neural network