An Active Detection Strategy Based on Dimensionality Reduction for False Data Injection Attacks in Cyber-Physical Systems

In recent years, different intelligent and undetectable cyber-attacks against networked control systems (e.g., replay and covert attacks) have been investigated. In this paper, we design a novel control architecture that prevents their existence. In particular, first, we propose to encode the sensor outputs into a randomly changing lower-dimensional space obtained by means of principal component analysis. Such a transformation ensures optimal information reconstruction on the controller's side, and it prevents the attacker from accessing the original sensor measurements, nullifying the possibility of perfect stealthy attacks. Then, on the controller's side, we design a passive Gaussian anomaly detector that leverages the output of an unknown input observer ad-hoc designed to estimate the system's states and unknown inputs simultaneously. It is formally shown that the proposed detection strategy is able to discover the presence of replay and covert attacks. Simulation results obtained considering a quadruple water tank system confirm the capability of the developed architecture.