Source Code Vulnerability Detection Using Vulnerability Dependency Representation Graph

Aiming at the fact that the existing source code vulnerability detection methods did not explicitly maintain the semantic information related to the vulnerability in the source code, which made it difficult for the vulnerability detection model to extract the vulnerability sentence features and had a high detection false positive rate, a source code vulnerability detection method based on the vulnerability dependency graph is proposed. Firstly, the candidate vulnerability sentences of the function were matched, and the vulnerability dependency representation graph corresponding to the function was generated by analyzing the multi-layer control dependencies and data dependencies of the candidate vulnerability sentences. Secondly, abstracted the function name and variable name of the code sentences node and generated the initial representation vector of the code sentence nodes in the vulnerability dependency representation graph. Finally, the source code vulnerability detection model based on the heterogeneous graph transformer was used to learn the context information of the code sentence nodes in the vulnerability dependency representation graph. In this paper, the proposed method was verified on three datasets. The experimental results show that the proposed method have better performance in source code vulnerability detection, and the recall rate is increased by 1.50%similar to 22.27%, and the F1 score is increased by 1.86%similar to 16.69%, which is better than the existing methods.