Work

Vulnerability Detection Model Based on Clustering-Aware Heterogeneous Code Graphs

Year: 2026

Type: article

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.

Source: Algorithms

DOI: 10.3390/a19070587

Authors: Shize Lu, Lianmei Wang, Jiangtao Huang

Institution: Nanning Normal University

Language: English

Topic: Software Engineering Research

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,930

Funder: National Natural Science Foundation of China