Secure Peer-to-Peer Federated Learning for Efficient Cyberattacks Detection in 5G and Beyond Networks

The Open radio access network (ORAN) supports the multiclass wireless services required in beyond 5th-generation (B5G) mobile networks. However, it also increases the threat surface, thus requiring enhanced cyberattack detection mechanisms. To do so, advanced Artificial Intelligence (AI) algorithms combined with RAN intelligent controllers (RICs) can be leveraged to detect cyberattacks, such as distributed denial-of-service (DDoS) attacks. Nevertheless, data privacy becomes a significant concern when using AI-based operations. To bypass this issue, secured Federated Learning (FL) can be leveraged. Specifically, training cyberattack detection models locally and securely communicating the models' data for aggregation would guarantee protection against eavesdropping. In addition, the usage of Peer-to-Peer (P2P) FL would allow to avoid the centralized FL's single point of failure. However, securing P2P FL with encryption/decryption or using the Secure Average Computation (SAC) would incur high communication costs that scale poorly with the number of FL clients. Hence, we propose in this paper a novel P2P FL strategy that guarantees secure FL, while significantly reducing the communication cost. Specifically, we incorporate client selection and transfer learning within the RIC-based P2P FL system to detect cyberattacks. Through experiments, we demonstrate our method's performances across different scenarios with both balanced and unbalanced dataset distributions. Finally, its superiority in terms of accuracy, robustness, and cost, compared to existing benchmarks, is illustrated.