Beyond Fidelity: Explaining Vulnerability Localization of Learning-based Detectors. (arXiv:2401.02686v1 [cs.CR])

Vulnerability detectors based on deep learning (DL) models have proven their
effectiveness in recent years. However, the shroud of opacity surrounding the
decision-making process of these detectors makes it difficult for security
analysts to comprehend. To address this, various explanation approaches have
been proposed to explain the predictions by highlighting important features,
which have been demonstrated effective in other domains such as computer vision
and natural language processing. Unfortunately, an in-depth evaluation of
vulnerability-critical features, such as fine-grained vulnerability-related
code …

address analysts beyond decision deep learning fidelity localization making predictions process security vulnerability