Multi-granularity Deep Vulnerability Detection Using Graph Neural Networks

The significance of vulnerability detection has grown increasingly crucial due to the escalating cybersecurity threats. Investigating automated vulnerability detection techniques to avoid high false positives and false negatives is an important issue in the current software security field. In recent years, there has been a substantial focus on deep learning-based vulnerability detectors, which have achieved remarkable success. To fill the gap in multi-granularity program representation, we propose MulGraVD, a deep learning-based vulnerability detector at the function level. MulGraVD captures the continuity and structure of the programming language by considering information at word, statement, basic block, and function granularity respectively. To overcome the constraint posed by hyperparameter layers in the information aggregation process of graph neural networks, MulGraVD serially passes information from coarse to fine granularity, which facilitates the mining of vulnerability patterns. Our experimental evaluation on FFMPeg+Qemu and ReVeal datasets shows that MulGraVD significantly outperforms existing state-of-the-art methods in terms of precision, recall, and F1 score, with an average improvement of 11.62% in precision, 27.69% in recall, and 19.71% in F1 score.