Towards Certified Malware Detection: Provable Guarantees Against Evasion Attacks
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 smoothing through feature ablation and targeted noise injection. During evaluation, our system analyzes an executable by generating multiple ablated variants, classifies them by using a smoothed classifier, and identifies the final label based on the majority vote. By analyzing the top-class voting distribution and the Wilson score interval, we derive a formal certificate that guarantees robustness within a specific radius against feature-spac...
| Authors | Nandakrishna Giri, Asmitha K. A., Serena Nicolazzo et al. |
|---|---|
| Primary category | cs.CR |
| Published | 22 April 2026 |
| Last updated at source | 22 April 2026 |
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