False Data Injection Attacks Against Partial Sensor Measurements of Networked Control Systems

This brief concerns the problem of false data injection (FDI) attacks against partial sensor measurements of a networked stochastic system. For a Kalman filter based output tracking control system with a residual-based anomaly detector, a partial FDI attack strategy is presented to deteriorate the system performance by injecting false signals into the feedback communication channel to tamper partial sensor measurements. The stealthiness condition of the attack as well as its impact on the closed-loop system is derived, which are quite different from those of the FDI attack against all sensor measurements given in the existing work. This may be helpful for guaranteeing the secure control of a networked system by protecting partial critical sensor measurements from FDI attacks. Two numerical examples are included to verify the theoretical results.

Automated summary

This paper addresses the issue of false data injection (FDI) attacks on partial sensor measurements of a networked stochastic system. A partial FDI attack strategy is presented, which injects false signals into the feedback communication channel to disrupt the performance of a Kalman filter based output tracking control system. The paper derives the stealthiness condition of the attack and its impact on the closed-loop system, which differs from existing work on FDI attacks against all sensor measurements. The research contributes to ensuring secure control of networked systems by protecting critical sensor measurements from FDI attacks.