Author

Lorenzo Cavallaro

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Preprint Sep 2026

Breaking Windows Malware Detection: A Comprehensive Evaluation of Problem-Space Adversarial Robustness

Problem-space evasion attacks have exposed critical weaknesses in machine learning-based malware detectors; yet, their evaluation remains fragmented across models, datasets, and attack methodologies, often neglecting domain-specific requirements such as executability and functionality preservation. We address this gap...

Mashal Zainab, Salijona Dyrmishi, Hamid Bostani et al. · 0 citations

#machine learning Preprint Aug 2026

REPLICANT: Learning Policies for Evading and Hardening Malware Detectors

This work presents Replicant, a deep reinforcement learning framework that learns the realistic task of evasion under a strict label-only black-box threat model and demonstrates that learning the task of evasion not only results in stronger attack performance but provides a better signal for hardening malware detectors...

Shae McFadden, Ilias Tsingenopoulos, Mario D'Onghia et al. · 0 citations