Residual-Based False Data Injection Attacks Against Multi-Sensor Estimation Systems

This paper investigates the security issue of multi-sensor remote estimation systems. An optimal stealthy false data injection (FDI) attack scheme based on historical and current residuals, which only tampers with the measurement residuals of partial sensors due to limited attack resources, is proposed to maximally degrade system estimation performance. The attack stealthiness condition is given, and then the estimation error covariance in compromised state is derived to quantify the system performance under attack. The optimal attack strategy is obtained by solving several convex optimization problems which maximize the trace of the compromised estimation error covariance subject to the stealthiness condition. Moreover, due to the constraint of attack resources, the selection principle of the attacked sensor is provided to determine which sensor is attacked so as to hold the most impact on system performance. Finally, simulation results are presented to verify the theoretical analysis.