Data-Injection Attacks in Stochastic Control Systems: Detectability and Performance Tradeoffs
Provides theoretical limits for attack detection in control systems, relevant to security of cyber-physical systems.
This paper characterizes fundamental limitations on detectability of data-injection attacks in stochastic control systems and quantifies the performance degradation a stealthy attacker can cause.
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.