arXiv:2509.15794v2 Announce Type: replace-cross Abstract: System identification in modern engineering systems faces emerging challenges from unanticipated adversarial attacks beyond existing detection mechanisms. In this work, we obtain a provably accurate estimate of the Markov parameter matrix of order $k$ to identify partially observed linear systems, in which the probability $p$ of having an attack at each time is smaller than $0.5$. We show that given the batch data accumulated up to time $T^*$, the $\ell_2$-norm estimator achieves an error with stepwise exponential decay as the attack probability $p$ decreases. Furthermore, low-dimensional attack structures can decouple the exponential decay order from the attack probability $p$, achieving an error decay that scales with $T^*$. We then propose a stochastic projected subgradient descent algorithm on streaming data that produces an estimate at each time $tSource：https://arxiv.org/abs/2509.15794
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