Fusion and detection for multi-sensor systems under false data injection attacks

Abstract

This article investigates a security problem in cyber–physical systems under multisensory framework. Data packets from each sensor are transmitted to a remote estimator through wireless channels which could be attacked by a attacker and injected false data. Based on the data of reliable sensors and the relativity between reliable and unreliable sensors, a revised multi-sensor Kalman filter fusion algorithm is proposed. Under the proposed algorithm, an optimal linear false data injection attack strategy which is more general with an arbitrary mean of Gaussian distribution is designed. To further improve the detection performance, an Expected SARSA-based attack detection algorithm is proposed. Finally, simulation results based on an unmanned aerial vehicle are provided to illustrate the feasibility and efficiency of the obtained results.