VulSPG: Vulnerability detection based on slice property graph representation learning

Understand

Vulnerability detection is an important issue in software security.

• Although various data-driven vulnerability detection methods have been proposed, the task remains challenging since the diversity and complexity of real-world vulnerable code in syntax and semantics make it difficult to extract vulnerable features with regular deep learning models, especially in analyzing a large program.
• Moreover, the fact that real-world vulnerable codes contain a lot of redundant information unrelated to vulnerabilities will further aggravate the above problem.
• To mitigate such challenges, we define a novel code representation named Slice Property Graph (SPG), and then propose VulSPG, a new vulnerability detection approach using the improved R-GCN model with triple attention mechanism to identify potential vulnerabilities in SPG.