Stealthy Deactivation of Safety Filters
CoRR(2024)
摘要
Safety filters ensure that only safe control actions are executed. We propose
a simple and stealthy false-data injection attack for deactivating such safety
filters; in particular, we focus on deactivating safety filters that are based
on control-barrier functions. The attack injects false sensor measurements to
bias state estimates to the interior of a safety region, which makes the safety
filter accept unsafe control actions. To detect such attacks, we also propose a
detector that detects biases manufactured by the proposed attack policy, which
complements conventional detectors when safety filters are used. The proposed
attack policy and detector are illustrated on a double integrator example.
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关键词
Safety Filter,Active Control,Multiple Integration,Injection Attacks,False Data Injection,False Data Injection Attacks,System Dynamics,Measurement Noise,Anomaly Detection,Interior Point,Cyber-physical Systems,Safe Set,Random Attack,Cyber Attacks
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