Defeating Eavesdropping Attacks with Inter-Cell Interference and Deep Reinforcement Learning

This paper introduces a novel joint user association and resource allocation framework to efficiently deal with eavesdropping attacks without requiring prior information about eavesdroppers. Specifically, the co-channel interference when reusing resource blocks is leveraged to disrupt the signal reception at eavesdroppers. To maximize the secure area, defined as the area where eavesdroppers cannot wiretap the channel due to co-channel interference, we first formulate the system by using the Markov decision process to capture the dynamics and uncertainty of mobile users and wireless communications. Then, a deep reinforcement learning (DRL)-based approach is proposed to obtain the joint optimal user association and resource allocation policy to utilize the co-channel interference and maximize the secure area. Extensive simulation results demonstrate that by intelligently associating users and allocating resource blocks to them, our proposed solution can help to effectively defeat eavesdropping attacks without requiring prior information of eavesdroppers which may not be readily available in practice. In addition, the proposed DRL-based algorithm can converge to the optimal policy quickly and achieve better performance compared to existing solutions.