基于贝叶斯网络模型推理分析的5G匿名干扰检测
Anonymous Jamming Detection in 5G with Bayesian Network Model Based Inference Analysis
AI总结：
本文提出一种基于协议栈信号参数的5G匿名干扰检测模型，结合监督与无监督学习实现高精度实时检测，并引入贝叶斯网络因果分析以增强透明性和领域知识注入。
摘要：
干扰与入侵检测在5G研究中至关重要，旨在维持可靠性、防止用户体验下降并避免基础设施故障。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。该系统利用监督学习和无监督学习实现实时、高精度的干扰检测，包括未知类型的干扰。监督模型的AUC达到0.964至1，相比之下，LSTM模型的AUC为0.923至1。然而，数据标注的需求限制了监督方法的应用。为解决这一问题，提出了一种基于自编码器的无监督异常检测方法，其AUC为0.987。该方法对对抗性训练样本具有抵抗能力。为了实现透明性和领域知识注入，引入了基于贝叶斯网络的因果分析。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
