Data-Injection Attacks in Stochastic Control Systems: Detectability and Performance Tradeoffs
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Abstract (arXiv)
Consider a stochastic process being controlled across a communication channel. The control signal that is transmitted across the control channel can be replaced by a malicious attacker. The controller is allowed to implement any arbitrary detection algorithm to detect if an attacker is present. This work characterizes some fundamental limitations of when such an attack can be detected, and quantifies the performance degradation that an attacker that seeks to be undetected or stealthy can introduce.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 Apr 2017.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 0; 2025: 1; 2026: 2.
4 papers.
- 2025Deceptive Exploration in Multi-armed Banditscited by 1
- 2021A Secure Learning Control Strategy via Dynamic Camouflaging for Unknown Dynamical Systems under Attackscited by 0
- 2026Fundamental Limits of Man-in-the-Middle Attack Detection in Model-Free Reinforcement Learningcited by 0
- 2026Dynamical low-rank equilibrium computation for stochastic games between advanced persistent threats and moving target defensecited by 0
- 2026Dynamical low-rank equilibrium computation for stochastic games between advanced persistent threats and moving target defense20 Sep 2026
- 2026Fundamental Limits of Man-in-the-Middle Attack Detection in Model-Free Reinforcement Learning29 Mar 2026
- 2025Deceptive Exploration in Multi-armed Bandits9 Oct 2025
- 2021A Secure Learning Control Strategy via Dynamic Camouflaging for Unknown Dynamical Systems under Attacks1 Feb 2021