Sequential Graph Neural Networks for Source Code Vulnerability Identification. (arXiv:2306.05375v1 [cs.CR])

Vulnerability identification constitutes a task of high importance for cyber
security. It is quite helpful for locating and fixing vulnerable functions in
large applications. However, this task is rather challenging owing to the
absence of reliable and adequately managed datasets and learning models.
Existing solutions typically rely on human expertise to annotate datasets or
specify features, which is prone to error. In addition, the learning models
have a high rate of false positives. To bridge this gap, in this paper, …

applications code code vulnerability cyber cyber security datasets functions high human identification large managed networks neural networks security solutions source code task vulnerability vulnerable