Vulnerability Detection Model Based on Clustering-Aware Heterogeneous Code Graphs

Abstract

Software vulnerabilities pose a severe threat to system security and stability. To reinforce the hierarchical structure of source code, enhance the distinctiveness of embedded features, and reduce feature confusion, we propose CADetect, a vulnerability detection model based on clustering-aware heterogeneous code graphs. First, a hierarchical clustering algorithm is applied to constrain and optimize the embedding vectors. This enhances the model’s ability to differentiate between diverse node and edge types within the heterogeneous code graphs, while streamlining code structures and reducing redundant information. Subsequently, multi-scale convolutions with varying kernel sizes are designed to extract vulnerability features at different granularities, effectively mitigating the information loss commonly caused by single-scale convolutions. Finally, extensive evaluations on the FFmpeg+Qemu, BigVul, and Reveal datasets demonstrate the effectiveness of the proposed model, and ablation studies confirm the specific contributions of the clustering algorithm and multi-scale convolutions.