User-Centric View of Unmanned Aerial Vehicle Transmission Against Smart Attacks

Abstract

Unmanned aerial vehicle (UAV) systems are vulnerable to smart attackers, who are selfish and subjective end-users and use smart radio devices to change their attack types and policies based on the ongoing UAV transmission and network states. In this paper, we apply prospect theory to formulate a subjective smart attack game for the UAV transmission, in which a smart attacker Eve makes subjective decisions to choose the attack type such as jamming, spoofing, and eavesdropping without knowing the attack detection accuracy of the UAV system, and the UAV transmit power on multiple radio channels is chosen to resist smart attacks. Reinforcement-learning-based UAV power allocation strategies are proposed to achieve the optimal power allocation against smart attacks without knowing the attack model and the channel model in the dynamic game. A deep Q-learning-based UAV power allocation strategy combines Q-learning and deep learning to accelerate the learning speed for the case with a large number of channel states and attack modes. Simulation results show that our proposed UAV power allocation strategy can suppress the attack motivation of subjective smart attackers and increase the secrecy capacity and the utility of the UAV system.