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. Our approach has at least two advantages over other methods. First, our proposed SPG can reflect the rich semantics and explicit structural information that may be relevance to vulnerabilities, while eliminating as much irrelevant information as possible to reduce the complexity of graph. Second, VulSPG incorporates triple attention mechanism in R-GCNs to achieve more effective learning of vulnerability patterns from SPG. We have extensively evaluated VulSPG on two large-scale datasets with programs from SARD and real-world projects. Experimental results prove the effectiveness and efficiency of VulSPG.
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