MSGVUL: Multi-semantic integration vulnerability detection based on relational graph convolutional neural networks

Software security has drawn extensive attention as software projects have grown increasingly large and complex. Since the traditional manual or equipment vulnerability detection technology cannot meet today's software development needs, there is a recognized need to create more effective techniques to address security issues. Although various vulnerability detection systems have been proposed, most are based only on serialization or graph representation, to inadequate effect. We propose a system, MSGVUL, that provides superior vulnerability detection using a new multi-semantic approach. MSGVUL uses versatile and efficient code slicing employing a search algorithm based on sensitive data and functions and innovatively constructs an SSVEC model to fully integrate the semantic and structural information into the code. We also developed a novel BAG model, made up of BAP and PAG frameworks, that enables the hierarchical extraction of code vulnerability representations from the graph and sequence levels. The MSGVUL model is evaluated on slice-level and function-level vulnerability datasets, and the results demonstrate that the MSGVUL method outperforms other state-of-the-art methods.