基于多模态特征融合的智能合约漏洞检测方法研究OA

Most of the smart contract vulnerability detection methods rely on single mode feature extraction,which leads to the problem of low detection accuracy due to insufficient key feature extraction.This paper proposes a smart contract vulnerability detection method based on multimodal feature fusion.Firstly,the construction of the control flow graph(CFG)is constructed by leveraging the abstract syntax tree(AST)trimmed at the source code layer and the data flow relationship based on the opcode layer,which is imported into the graph attention network(GAT)to extract two types of static features.Secondly,the fuzzing test report generated by echidna,a dynamic detection tool,is used to extract path coverage,state changes and other information to build a graph model,and the dynamic features are extracted by graph neural network(GNN).Finally,the extracted static and dynamic features are fused and input into CNN bilstm att model for vulnerability detection,and relevant experiments are carried out on 47 398 smart contracts.Experimental results show that compared with eight mainstream detection methods,such as SmartCheck,Mythril,Oyente,Bi-GGNN,ASTNN,DR-GCN,SVCB and CBGRU,the accuracy,recall and F1 value of this method in re-entry vulnerability,timestamp vulnerability,integer overflow vulnerability and Tx.origin vulnerability are increased by 50.26％,59.54％and 58.40％.