AI RESEARCH
Secure Reinforcement Learning: On Model-Free Detection of Man in the Middle Attacks
arXiv CS.LG
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ArXi:2603.27592v1 Announce Type: cross We consider the problem of learning-based man-in-the-middle (MITM) attacks in cyber-physical systems (CPS), and extend our previously proposed Bellman Deviation Detection (BDD) framework for model-free reinforcement learning (RL). We refine the standard MDP attack model by allowing the reward function to depend on both the current and subsequent states, thereby capturing reward variations induced by errors in the adversary's transition estimate.