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.
Anomaly de- tection in vehicular networks using causality-aware graph convolutional networks (CA-GCN)
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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.