Explainability Guided Adversarial Evasion Attacks on Malware Detectors

arXiv:2405.01728v1 Announce Type: new
Abstract: As the focus on security of Artificial Intelligence (AI) is becoming paramount, research on crafting and inserting optimal adversarial perturbations has become increasingly critical. In the malware domain, this adversarial sample generation relies heavily on the accuracy and placement of crafted perturbation with the goal of evading a trained classifier. This work focuses on applying explainability techniques to enhance the adversarial evasion attack on a machine-learning-based Windows PE malware detector. The explainable tool identifies the …

accuracy adversarial artificial artificial intelligence arxiv attacks critical cs.cr domain evasion evasion attacks focus focus on security goal intelligence malware paramount research sample security