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cs.LG2026
REPLICANT: Learning Policies for Evading and Hardening Malware Detectors
Shae McFadden, Ilias Tsingenopoulos, Mario D'Onghia +5
To determine the real-world effectiveness of machine learning based malware detection, it is vital to evaluate its robustness against highly capable adversaries. However, state-of-…
cs.LG2026
SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecurity
Shae McFadden, Myles Foley, Elizabeth Bates +5
Deep Reinforcement Learning (DRL) has achieved remarkable success in domains requiring sequential decision-making, motivating its application to cybersecurity problems. However, tr…