Networks, threat models, and malicious actors are advancing quickly. With the increased deployment of the 5G networks, the security issues of the attached 5G physical devices have also increased. Therefore, artificial intelligence based autonomous end-to-end security design is needed that can deal with incoming threats by detecting network traffic anomalies. To address this requirement, in this research, we used a recently published 5G traffic dataset, 5G-NIDD, to detect network traffic anomalies using machine and deep learning approaches. First, we analyzed the dataset using three visualization techniques: t-Distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), and Principal Component Analysis (PCA). Second, we reduced the data dimensionality using mutual information and PCA techniques. Third, we solve the class imbalance issue by inserting synthetic records of minority classes. Last, we performed classification using six different classifiers and presented the evaluation metrics. We received the best results when K-Nearest Neighbors classifier was used: accuracy (97.2%), detection rate (96.7%), and false positive rate (2.2%).

翻译：网络、威胁模型及恶意攻击者的演进速度日益加快。随着5G网络部署规模扩大，其物理设备的安全风险也随之增加。因此，需要构建基于人工智能的端到端自主安全架构，通过检测网络流量异常来应对潜在威胁。为满足这一需求，本研究采用近期发布的5G流量数据集5G-NIDD，运用机器学习和深度学习方法检测网络流量异常。首先，我们通过三种可视化技术分析数据集：t分布随机邻域嵌入（t-SNE）、均匀流形逼近与投影（UMAP）及主成分分析（PCA）。其次，利用互信息和PCA技术降低数据维度。再次，通过插入少数类合成样本解决类别不平衡问题。最后，采用六种不同分类器进行分类并呈现评估指标。当使用K近邻分类器时取得最优结果：准确率97.2%，检测率96.7%，假阳性率2.2%。