SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities

Zhen Li, Deqing Zou, Shouhuai Xu, Hai Jin, Yawei Zhu, Zhaoxuan Chen, Sujuan Wang & Jialai Wang
We propose a general framework for using deep learning to detect vulnerabilities, named SySeVR. For evaluate the SySeVR, we collect the Semantics-based Vulnerability Candidate (SeVC) dataset, which contains all kinds of vulnerabilities that are available from the National Vulnerability Database (NVD) and the Software Assurance Reference Dataset (SARD). At a high level, the Syntax-based Vulnerability Candidate (SyVC) representation corresponds to a piece of code in a program that may be vulnerable based on a syntax...
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