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.

随着无线网络干扰攻击频率的增加，传统的本地干扰检测方法无法应对高级干扰攻击。为了最大限度地提高基于机器学习（ML）的检测方法的干扰检测性能，需要一个反映每个本地节点的本地检测结果的全局模型。本研究提出了一种基于机器学习的协作集群 (MLCC) 技术，旨在有效检测和应对使用智能中继器的超 5G 网络中的干扰。 MLCC算法通过根据每个本地节点确定的干扰检测结果创建和更新全局ML模型来优化检测率。通过智能中继器和接入点之间的负载平衡来优化网络性能，并选择最佳路径以避免干扰。实验结果表明，MLCC将检测率和吞吐量分别提高了5.21%和26.35%，同时将能耗和延迟分别降低了76.68%和7.14%。