arxiv
PublishedApril 24, 2026 at 4:00 AM
—neutral
Towards Certified Malware Detection: Provable Guarantees Against Evasion Attacks
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arXiv:2604.20495v1 Announce Type: cross Abstract: Machine learning-based static malware detectors remain vulnerable to adversarial evasion techniques, such as metamorphic engine mutations. To address this vulnerability, we propose a certifiably robust malware detection framework based on randomized
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