Learning-based secure communication against active eavesdropper in dynamic environment

In this study, the authors propose a learning-based approach to improve the security of the authors' considered communication system in a dynamic environment, where a source transmits information to a legitimate receiver in the presence of an active eavesdropper. Additionally, they assume that the source has to harvest energy from the environment to support its communication. Due to the dynamic of the environment, both the harvested energy and the channel vary over time, requiring a dynamic transmission strategy that follows the changes. In order to improve the security performance, they first analyse how to select the optimal transmission parameters in hindsight, and then they propose to combine the Q-learning algorithm and the expert advice method to maximise the cumulative reward in the dynamic environment. They also introduce an improved learning-based approach, which accelerates the convergence of their approach. The simulation results show that their proposed learning-based approach helps the legitimate nodes learn a beneficial transmission strategy to obtain a larger cumulative reward.