Adversarial Game Against Hybrid Attacks in UAV Communications With Partial Information

Abstract

Unmanned aerial vehicle (UAV) communications are vulnerable to smart attacks, where the attacker can change the attack mode (e.g., eavesdropping and jamming) via smart radio devices. To ensure secure transmissions against hybrid attacks, UAVs may transmit confidential information and misleading information alternately. In this paper, we consider a dynamic anti-hybrid attack framework with trajectory optimization, where both a UAV and an attacker attempt to find their optimal trajectories without knowing the information type and the attack mode of each other. Given the major challenge due to incomplete knowledge (i.e., each agent knows only its own information), we establish an adversarial game with partial-observation feature to formulate an optimization problem, and propose a counterfactual regret minimization learning scheme to achieve the correlated equilibrium for both the UAV and attacker. Simulation results validate the superiority of our scheme over a benchmark in UAV communication scenarios with partial information.