Jamming and intrusion detection are critical in 5G research, aiming to maintain reliability, prevent user experience degradation, and avoid infrastructure failure. This paper introduces an anonymous jamming detection model for 5G based on signal parameters from the protocol stacks. The system uses supervised and unsupervised learning for real-time, high-accuracy detection of jamming, including unknown types. Supervised models reach an AUC of 0.964 to 1, compared to LSTM models with an AUC of 0.923 to 1. However, the need for data annotation limits the supervised approach. To address this, an unsupervised auto-encoder-based anomaly detection is presented with an AUC of 0.987. The approach is resistant to adversarial training samples. For transparency and domain knowledge injection, a Bayesian network-based causation analysis is introduced.

翻译：干扰与入侵检测是5G研究中的关键问题，旨在维持系统可靠性、避免用户体验降级并防止基础设施故障。本文提出了一种基于协议栈信号参数的5G匿名干扰检测模型。该系统采用监督学习与无监督学习实现实时高精度干扰检测（包括未知类型干扰）。监督模型AUC达到0.964~1，而LSTM模型AUC为0.923~1。然而，数据标注需求限制了监督方法的适用性。为此，本文引入基于无监督自编码器的异常检测方法，其AUC达0.987，且对对抗训练样本具有鲁棒性。为提升透明性并注入领域知识，进一步提出基于贝叶斯网络的因果分析机制。