Fundamental Limits of Man-in-the-Middle Attack Detection in Model-Free Reinforcement Learning
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. We also derive an optimal system-identification strategy for the adversary that minimizes detectable value deviations. Further, we prove that the agent's asymptotic learning time required to secure the system scales linearly with the adversary's l...
| Authors | Rishi Rani, Massimo Franceschetti |
|---|---|
| Primary category | eess.SY |
| Published | 29 March 2026 |
| Last updated at source | 1 April 2026 |
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