SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design
Asymmetric reward design for deep-RL ransomware detectors prioritises minimising missed infections over false alarms—a practical alignment of ML objectives with SOC operational reality.
Summary written by editorial AI · Source link below
arXiv:2607.06880v1 Announce Type: new Abstract: Ransomware detection is a security-critical task in which false negatives and false positives have unequal operational consequences. Conventional machine learning detectors often use symmetric objectives that penalize missed ransomware detections and benign false alarms equally, although a false negative can cause irreversible encryption, operational disruption, and high recovery cost, whereas a false positive is usually reversible. This study pro
Editorial Analysis
Standard symmetric loss functions in ML detectors treat missed ransomware and false alarms equally, which misaligns with the far higher cost of an undetected infection.
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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