Auditable Machine Unlearning for Privacy-Compliant Ransomware Detection Using Multi-Shard SISA and Deep Reinforcement Learning

Ransomware poses an escalating cybersecurity threat as attackers continuously modify behavioral patterns to evade static defenses. Although existing machine learning-based detectors often achieve strong predictive performance, they generally assume fixed training data and do not support the selective removal of previously learned samples. This limitation conflicts with privacy regulations such as the GDPR and CCPA, which require the removal of sensitive user data upon request. To address this ch

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ethics.ai (7 July 2026), “Auditable Machine Unlearning for Privacy-Compliant Ransomware Detection Using Multi-Shard SISA and Deep Reinforcement Learning,” evidence record 3019, https://ethics.ai/record/3019 (originally published by arXiv cs.CR (AI security)).

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