Analysis and Mitigation of Data injection Attacks against Data-Driven Control
By: Sribalaji C. Anand
Potential Business Impact:
Stops bad data from making machines unstable.
This paper investigates the impact of false data injection attacks on data-driven control systems. Specifically, we consider an adversary injecting false data into the sensor channels during the learning phase. When the operator seeks to learn a stable state-feedback controller, we propose an attack strategy capable of misleading the operator into learning an unstable feedback gain. We also investigate the effects of constant-bias injection attacks on data-driven linear quadratic regulation (LQR). Finally, we explore potential mitigation strategies and support our findings with numerical examples.
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