Optimal two-channel switching false data injection attacks against remote state estimation of the unmanned aerial vehicle cyber-physical system

Abstract

A security issue with multi-sensor unmanned aerial vehicle (UAV) cyber physical systems (CPS) from the viewpoint of a false data injection (FDI) attacker is investigated in this paper. The FDI attacker can employ attacks on feedback and feed-forward channels simultaneously with limited resource. The attacker aims at degrading the UAV CPS's estimation performance to the max while keeping stealthiness characterized by the Kullback-Leibler (K-L) divergence. The attacker is resource limited which can only attack part of sensors, and the attacked sensor as well as specific forms of attack signals at each instant should be considered by the attacker. Also, the sensor selection principle is investigated with respect to time invariant attack covariances. Additionally, the optimal switching attack strategies in regard to time variant attack covariances are modeled as a multi-agent Markov decision process (MDP) with hybrid discrete-continuous action space. Then, the multi-agent MDP is solved by utilizing the deep Multi-agent parameterized Q-networks (MAPQN) method. Ultimately, a quadrotor near hover system is used to validate the effectiveness of the results in the simulation section.