Defense Filters for Robust Learning of Partially Observed Dynamical Systems
This work proposes a defense-filtering framework to robustly estimate unknown system dynamics from partial observations when an adversary injects noise with a probability less than 0.5 at each time step. The method uses a defense filter matrix constructed using knowledge of the observation matrix to restrict attacks into a lower-dimensional subspace, ensuring provable error bounds for recovering the system's Markov parameter matrix G*.