Vulnerability Detection Based on Attention Mechanism and Deep Graph Convolutional Network

To address the problem of incomplete graph structure features due to the lack of contextual information in existing graph neural network-based vulnerability mining methods and the problem of over-smoothing that prevents the model from learning higher-order features of the graph structure resulting in poor prediction performance, a vulnerability detection method based on deep graph convolutional networks and attention mechanism, PSG-GCNIIAT, is proposed. PSG fuses order relational graphs on the basis of program dependency graphs so that code statements have the ability to sense their contextual information, and generates embedding vectors of code lines by abstracting syntax trees to achieve deep structure feature extraction of graph nodes. GCNIIAT uses a deep graph convolutional network, GCNII, combined with a graph attention mechanism to more effectively identify graph structure features of program slices associated with vulnerabilities. The experimental results show that the vulnerability detection model GCNIIAT proposed in this paper has obvious advantages over Vuldeepecker, GCN and GGNN in accuracy and F1 value indicators, and can effectively improve the performance of program vulnerability detection.