Machine Learning-Based Cooperative Clustering for Detecting and Mitigating Jamming Attacks in beyond 5G Networks

Abstract

As the frequency of jamming attacks on wireless networks has increased, conventional local jamming detection methods cannot counter advanced jamming attacks. To maximize the jammer detection performance of machine learning (ML)-based detection methods, a global model that reflects the local detection results of each local node is necessary. This study proposes an ML-based cooperative clustering (MLCC) technique aimed at effectively detecting and countering jamming in beyond-5G networks that utilize smart repeaters. The MLCC algorithm optimizes the detection rate by creating and updating a global ML model based on the jammer detection results determined by each local node. The network performance is optimized through load balancing among the smart repeaters and access points, and the best path is selected to avoid jammers. The experimental results demonstrate that the MLCC improves the detection rate and throughput by up to 5.21% and 26.35%, respectively, while reducing the energy consumption and latency by up to 76.68% and 7.14%, respectively.