Attack-Resilient Connectivity Game for UAV Networks using Generative Adversarial Learning

The continuous link connectivity is critical for the efficient collaboration of multiple unmanned aerial vehicles (UAVs). However, the UAV communication environments are not only harsh, but are also confronted with the threats of smart attackers, which pose great barriers in maintaining the links unblocked. In this paper, we leverage the paradigm of the Generative Adversarial Network (GAN) to formulate an attack-resilient connectivity game between a pair of neighboring UAVs and an attacker. In the three-agent adversary game, the attacker acts as the generator, which attempts to generate highly approximate information as the UAVs so as to maximize its jamming capability; while the pairwise UAVs act as the discriminators, which attempt to enhance the capability of refusing the fake information (i.e., the opponent's attack). As the state-of-the-art GAN learning algorithms suffer from the instability dilemma (i.e., either with the unsuccessful convergence or with the low generation/discrimination performance), we incorporate the conditional GAN with the least square objective loss function as well as the mean square error such that the attacker can improve the detection capability from UAVs' historical activity patterns and the UAVs can accordingly adjust the connectivity strategy. We validate the effectiveness of the proposed algorithm through extensive evaluations. Results demonstrate that the proposed algorithm can improve the convergence efficiency, reduce the connection latency, and enhance the attack-resilience capability significantly.