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cs.CR2026
DDQN-MLP: An Explainable and Adversarially Robust DRL-Guided Adaptive Learning Framework for Ransomware Detection
Jannatul Ferdous, Rafiqul Islam, Arash Mahboubi +1
Ransomware detection remains challenging because modern variants exhibit diverse, evasive, and partly benign-like behaviors that undermine fixed supervised learning objectives. Thi…
cs.CR2026
TL-RL-FusionNet: An Adaptive and Efficient Reinforcement Learning-Driven Transfer Learning Framework for Detecting Evolving Ransomware Threats
Jannatul Ferdous, Rafiqul Islam, Arash Mahboubi +1
Modern ransomware exhibits polymorphic and evasive behaviors by frequently modifying execution patterns to evade detection. This dynamic nature disrupts feature spaces and limits t…