A survey of source code vulnerability detection research based on graph neural networks
With the widespread application of open-source software across various domains,source code vulnerabilities have led to a series of serious security issues.Given the potential threats these vul-nerabilities pose to computer systems,detecting source code vulnerabilities in software to prevent net-work attacks is a crucial research area.To achieve automated detection and reduce human labor costs,researchers have proposed numerous traditional deep learning-based methods.However,these methods mostly treat source code as natural language sequences and do not adequately consider the structural in-formation of the code,limiting their detection effectiveness.In recent years,methods for detecting source code vulnerabilities based on code graph representation and graph neural networks have emerged.This paper provides a comprehensive review of the application of graph neural networks in source code vulnerability detection and proposes a general framework for source code vulnerability detection based on graph neural networks.Starting from three levels of vulnerability detection granularity:file-level,function-level,and slice-level,the existing methods and relevant datasets are systematically summarized and elucidated.Finally,the challenges faced by this field are discussed,and potential research directions for the future are outlined.
万方数据
维普
