Feature-Wise Modulation Graph Neural Network Smart Contract Vulnerability Detection Based on Source Code

With the wide use of blockchain technology, the security of smart contracts has attracted wide attention. The conversion of smart contract source code to bytecode will lose some semantic information, and the existing deep learning vulnerability detection methods cannot detect reentrancy vulnerabilities and timestamp vulnerabilities well. This paper proposed a smart contract source vulnerability detection method（GNN-film） based on feature-wise modulation graph neural network. Firstly, the characteristics of reentrancy vulnerabilities and timestamp vulnerabilities were analyzed, the graph structure was constructed and simplified by using smart contract source code. Secondly, constructing the model of feature-wise linear modulation graph neural network, and getting accurate representation of contract vulnerability features by using the powerful feature modulation ability of the model. Finally, put simplified graph structure data into the model to obtain the detection results. The experimental results show that the detection accuracy of reentrancy vulnerability and timestamp vulnerability is 91. 00% and 91. 64% respectively, which is 4. 20 and 9. 70 percentage points higher than that of graph neural network method. It is proved that the detection ability of this method for related vulnerabilities is better than other detection tools.