Explainability-Informed Targeted Malware Misclassification

arXiv:2405.04010v1 Announce Type: new
Abstract: In recent years, there has been a surge in malware attacks across critical infrastructures, requiring further research and development of appropriate response and remediation strategies in malware detection and classification. Several works have used machine learning models for malware classification into categories, and deep neural networks have shown promising results. However, these models have shown its vulnerabilities against intentionally crafted adversarial attacks, which yields misclassification of a malicious file. Our paper explores such adversarial vulnerabilities …

arxiv attacks classification critical cs.cr detection development machine machine learning machine learning models malware malware attacks malware classification malware detection networks neural networks remediation research research and development response results strategies targeted malware