Optimal Node Selection for Hybrid Attack in Underwater Acoustic Sensor Networks: A Virtual Expert-Guided Bandit Algorithm

In this study, adversarial graph bandit theory is used to rapidly select the optimal attack node in underwater acoustic sensor networks (UASNs) with unknown topology. To ensure the flexibility and elusiveness of underwater attack, we propose a bandit-based hybrid attack mode that combines active jamming and passive eavesdropping. We also present a virtual expert-guided online learning algorithm to select the optimal node without priori topology information and complex calculation. The virtual expert mechanism is proposed to guide the algorithm learning. The expert establishes a virtual topology configuration, which addresses the blind exploration and energy consumption of attackers to a large extent. With the acoustic broadcast characteristic, we also put forward an expert self-updating method to follow the changes of real networks. This method enables the algorithm to commendably adapt to the dynamic environments. Simulation results verify the strong adaptability and robustness of the proposed algorithm.