Abstract
As software vulnerabilities have surged, efforts to discover them have increased. The syntactic and semantic information of a program is required to detect vulnerabilities. Each information can be represented as a graph, such as Abstract Syntax Tree and Program Dependency Graph. In this paper, the program representations were extracted using various static analysis tools, including Clang Static Analyzer, Joern, and SVF, and compared using Graph Neural Networks to select the appropriate representations for vulnerability detection in C/C++. From the comparison, PDG shows the best performance among the multiple representations. This result indicates a suitable program representation and a tool for vulnerability detection that can be utilized in research utilizing graph neural networks.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEIE Transactions on Smart Processing & Computing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
