C-MADF learns a structural causal model to restrict response actions in an MDP and uses dual blue-red RL policies to achieve 1.8% false-positive rate and 0.979 F1 on the CICIoT2023 dataset.
A new intrusion detection system based on moth-flame optimizer algorithm,
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Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning
C-MADF learns a structural causal model to restrict response actions in an MDP and uses dual blue-red RL policies to achieve 1.8% false-positive rate and 0.979 F1 on the CICIoT2023 dataset.