Cooperative Physical Layer Authentication With Reputation-Inspired Collaborator Selection

Machine learning (ML)-based physical layer authentication (PLA) has attracted much attention since neural networks can be constructed to identify channel characteristics in complex wireless environments. This enables high-authentication performance and lightweight deployment in the Internet of Things (IoTs). Due to the booming growth of IoT connections, the workload of the central authenticator increases significantly. As a result, resource-constrained terminals are unable to independently handle the computationally complex ML task. Therefore, cooperative PLA (CoPLA), which introduces multiple supervised nodes as task-sharing entities, is emerged as a promising solution to address this concern. However, in existing CoPLA studies, some critical issues have been overlooked. For example, the consideration of which collaborative nodes are eligible or best suited for cooperation to maximize the authentication gains. Moreover, the security threats posed by untrusted collaborators are equally challenging. In this article, we propose a federated learning (FL)-based CoPLA scheme that utilizes a group of edge devices to jointly build an authenticator. This ensures privacy preservation and higher robustness. To figure out the optimal collaborator selection in CoPLA, an adaptive search procedure via reinforcement learning (RL) is customized. Furthermore, we introduce a lightweight reputation estimation method to evaluate each collaborator’s credibility, thereby uncovering underperforming devices or hidden internal attackers. Finally, simulations and real-world experiments are carried out. The results show that the authentication accuracy of our scheme is 9.52% higher than that of blind cooperation. And, it outperforms other existing CoPLA schemes in terms of time efficiency and robustness.