The paper proposes a co-evolving red-blue reinforcement learning environment for network intrusion detection and claims the blue agent recovers up to 30% accuracy after just 2 to 3 adaptation steps with 25 to 30 samples per step.
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Adapting Under Fire: Multi-Agent Reinforcement Learning for Adversarial Drift in Network Security
The paper proposes a co-evolving red-blue reinforcement learning environment for network intrusion detection and claims the blue agent recovers up to 30% accuracy after just 2 to 3 adaptation steps with 25 to 30 samples per step.