Vulnerability Detection by Sequential Learning of Program Semantics via Graph Attention Networks

Vulnerability detection is a crucial aspect of protecting software systems from cyber attacks. However, some types of vulnerabilities are difficult to detect and require analyzing the source code from multi-views. To address this, we propose a general and easily extensible framework, SGVD(Sequential Graph Attention Networks for Vulnerability Detection). SGVD consists of a sequential module that uses the GAT to learn the semantic representations of the code and a novel Fused-Prediction module that extracts useful features from the multi-view source code. We evaluated this framework on a dataset that includes two large-scale open-source C projects. The experiments showed that SGVD had a superior performance compared to the existing advanced graph learning vulnerability detection tools Devign and ReGVd,with an average increase of 12.25% in Accuracy, 13.65% in Precision, 12.04% in F1 score, and 9.14% in Recall.