Beyond Fidelity: Explaining Vulnerability Localization of Learning-based Detectors

arXiv:2401.02686v2 Announce Type: replace
Abstract: 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, …

address analysts arxiv beyond cs.cr cs.lg cs.se decision deep learning fidelity localization making process security vulnerability