SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design 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 irreversi…

https://arxiv.org/abs/2607.06880

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• md50
• url0
• ipv40
• ipv60
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