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Asymmetric reward design in deep reinforcement learning can drastically reduce false negatives in ransomware detection, achieving a remarkable 67.6% improvement over traditional methods.
Achieving near-perfect ransomware detection while ensuring compliance with privacy regulations through efficient and auditable unlearning methods.
An RL agent can dynamically adapt a transfer learning model to prioritize complex ransomware samples, boosting detection accuracy by up to 2.5% while slashing training time and RAM usage.