3 papers
cs.CR2026
SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design
Jannatul Ferdous, Rafiqul Islam, Md Zahidul Islam
Ransomware detection is a security-critical task in which false negatives and false positives have unequal operational consequences. Conventional machine learning detectors often u…
cs.CR2026
Auditable Machine Unlearning for Privacy-Compliant Ransomware Detection Using Multi-Shard SISA and Deep Reinforcement Learning
Jannatul Ferdous, Rafiqul Islam, Md Zahidul Islam
Ransomware poses an escalating cybersecurity threat as attackers continuously modify behavioral patterns to evade static defenses. Although existing machine learning-based detector…
cs.CR2026
Privacy-Aware Machine Unlearning with SISA for Reinforcement Learning-Based Ransomware Detection
Jannatul Ferdous, Rafiqul Islam, Md Zahidul Islam
Ransomware detection systems increasingly rely on behavior-based machine learning to address evolving attack strategies. However, emerging privacy compliance, data governance, and…