面向私有5G网络干扰检测的、基于GLRT的单类分类方法
One-Class Classification as GLRT for Jamming Detection in Private 5G Networks
AI总结：
针对5G网络易受干扰攻击的问题，提出用实现GLRT的CNN作为二类分类器，结合真实合法信号与模拟干扰的人工数据集训练，经私有5G网络实验验证可有效检测干扰攻击。
摘要：
5G移动网络易受干扰攻击，这类攻击可能危及工业自动化等高价值应用。本文提出用专用设备分析无线电信号以检测干扰攻击。我们采用一种学习方法，检测器为实现广义似然比检验（GLRT）的卷积神经网络（CNN）。为此，我们使用两个数据集将CNN训练为二类分类器：一个是真实合法信号数据集，另一个是人工生成的数据集，人工数据集的生成模拟了不同类型的干扰信号，以此让训练得到的分类器实现GLRT。我们利用从私有5G网络获取的实验数据和多种干扰信号评估了该检测器的性能，结果表明该技术在检测攻击方面具备有效性。
英文摘要：
5G mobile networks are vulnerable to jamming attacks that may jeopardize valuable applications such as industry automation. In this paper, we propose to analyze radio signals with a dedicated device to detect jamming attacks. We pursue a learning approach, with the detector being a CNN implementing a GLRT. To this end, the CNN is trained as a two-class classifier using two datasets: one of real legitimate signals and another generated artificially so that the resulting classifier implements the GLRT. The artificial dataset is generated mimicking different types of jamming signals. We evaluate the performance of this detector using experimental data obtained from a private 5G network and several jamming signals, showing the technique's effectiveness in detecting the attacks.
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。
