Two‐channel false data injection attacks on multi‐sensor remote state estimation

Abstract

AbstractThis paper investigates a security problem in cyber‐physical systems (CPSs) under multisensory framework. In the considered system, the feedback and feed‐forward channels may suffer from false data injection (FDI) attacks. The attacker aims at disturbing the systems by modifying the sensors' measurements and injecting external control inputs while keep ( )‐stealthiness. Two types of multi‐sensor fusion Kalman filters, which are equivalent in the absence of attacks, are adopted by the remote state estimator. When the system is maliciously attacked, the necessary and sufficient condition is obtained to guarantee the equivalence of the sequential Kalman filter and data compression Kalman filter under two‐channel attacks. Moreover, suboptimal ( )‐stealthiness attack strategies for these Kalman filters are obtained by applying two‐stage optimization method, convex analysis and monotonic optimization techniques. Finally, simulations on a numerical example of a quadrotor are carried out to validate the feasibility of theoretical results.