Structural Semantic Enhancement: Better integrating code semantics for vulnerability detection

Abstract

Code vulnerability detection is particularly critical in software development and maintenance because it may prevent software instability, data leakage, or more serious security threats. Traditional code vulnerability detection methods usually rely on static analysis. While static analysis covers the entire code base and detects early errors, it may struggle with highly complex code structures, leading to potential false positives or false negatives. Deep learning has introduced new opportunities for detecting vulnerabilities but faces challenges with complex code structures and logical relationships. Efforts to integrate natural language processing embeddings into models like Graph Neural Networks aim to enhance semantic understanding but depend on the quality of the NLP model and embeddings.

To address these challenges, we propose a methodology centered around the Structural Semantic Enhancement Method (SSEM), which combines the semantic understanding of deep learning with structured code information provided by static analysis. Specifically, our method extracts the key information of control flow graphs and data dependency graphs and designs specialized SSEM with attention mechanisms. Based on two large-scale datasets, including more than 40,000 code snippets, we experimentally validated the effectiveness of the proposed method. Experimental results show that our method performs better in identifying potential vulnerabilities in code compared to traditional deep learning methods and advanced deep learning vulnerability detection models.