Detection Against Linear Deception Attacks on Multi-Sensor Remote State Estimation

In this paper, a security problem in cyberphysical systems (CPS) is studied. A remote state estimation process using multiple sensors is considered. The measurement innovation packets from each sensor, which may be modified by a malicious attacker, are sent to a remote fusion center through wireless communication channels. To avoid being detected by typical bad data detectors at the remote estimator's side, the attacker would maintain the statistical properties of the measurements. Based on the information extracted from the trusted sensors and the correlations between the trusted sensors and the suspicious sensors, we propose three sequential data verification and fusion procedures for different detection information scenarios. The corresponding impacts of possible attacking patterns on the estimation performance under different detectors are analyzed explicitly. Simulations are provided to illustrate the developed results.