REPLICANT: Learning Policies for Evading and Hardening Malware Detectors
REPLICANT uses reinforcement learning to co-train evasion and hardening policies for ML malware detectors, modelling more realistic adversaries than current attack benchmarks.
Summary written by editorial AI · Source link below
arXiv:2608.28499v1 Announce Type: cross Abstract: 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-the-art attacks do not effectively model realistic adversaries, as they often assume access to privileged information such as the training data, feature space, or confidence scores of the target. In this work, we present Replicant, a deep reinforcement learning framewo
Editorial Analysis
ML-based malware detection is increasingly deployed in enterprise SOCs; realistic adversarial evaluation reveals whether those tools withstand capable attackers.
Challenge your ML-based detection vendors to demonstrate robustness against RL-trained evasion strategies, not just signature-based test sets.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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