iGnnVD: A novel software vulnerability detection model based on integrated graph neural networks

Abstract

Software vulnerability detection is a challenging task in the security field, the boom of deep learning technology promotes the development of automatic vulnerability detection. Compared with sequence-based deep learning models, graph neural network (GNN) can learn the structural features of code, it performs well in the field of vulnerability detection for source code. However, different GNNs have different detection results for the same code, and using a single kind of GNN may lead to high false positive rate and false negative rate. In addition, the complex structure of source code causes single GNN model cannot effectively learn their depth feature, thereby leading to low detection accuracy. To solve these limitations, we propose a software vulnerability detection model called iGnnVD based on the integrated graph neural networks. In the proposed iGnnVD model, the base detectors including GCN, GAT and APPNP are first constructed to capture the bidirectional information in the code graph structure with bidirectional structure; And then, the residual connection is used to aggregate the features while retaining the features each time; Finally, the convolutional layer is used to perform the aggregated classification. In addition, an integration module that analyzes the detection results of three detectors for final classification is designed using a voting strategy to solve the problem of high false positive rate and false negative rate caused by using a single kind of base detector. We perform extensive experiments on three datasets and experimental results show that the proposed iGnnVD model can improve the detection accuracy of vulnerabilities in source code as well as reduce the false positive rate and false negative rate compared with existing deep learning-based vulnerability detection models, it also has good stability.