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%).
Passive Hybrid Network-Based Intrusion Detection System (Hybrid-NIDS) Combining Suricata and Random Forest
Ho Chi Minh City College of Transport（胡志明市交通学院）；Academy of Cryptography Techniques, Ho Chi Minh City Campus（密码技术学院胡志明市校区）；Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT)（胡志明市理工大学计算机科学与工程系）；Vietnam National University Ho Chi Minh City（越南国家大学胡志明市分校）
Flow Exporter Impact on Intelligent Intrusion Detection Systems
School of Engineering, Polytechnic of Porto (ISEP/IPP)（波尔图理工学院工程学院）；Porto Research, Technology & Innovation Center (PORTIC), Polytechnic of Porto (IPP)（波尔图理工学院波尔图研究、技术与创新中心）
Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction
International University of Business Agriculture and Technology（国际商业农业技术大学）；Jagannath University（贾甘纳特大学）；Deakin University（迪肯大学）；University of New South Wales（新南威尔士大学）；Imam Mohammad Ibn Saud Islamic University（伊玛目穆罕默德·伊本·沙特伊斯兰大学）；The University of Queensland（昆士兰大学）
Elevating Intrusion Detection and Security Fortification in Intelligent Networks through Cutting-Edge Machine Learning Paradigms
Department of Computer Science and Technology, Beijing Institute of Technology（计算机科学与技术系，北京理工大学）；Department of Quantitative Methods and Economic Informatics, Faculty of Operation and Economics of Transport and Communication, University of Zilina（定量方法与经济信息学系，交通与通信运营经济学院，扎林大学）；Department of Telecommunications, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava（电信系，电气工程与计算机科学学院，奥斯特拉瓦技术大学）；Department of AI and Software, Gachon University（人工智能与软件系，高丽大学）；Department of Applied Informatics, Kimyo International University in Tashkent（应用信息学系，塔什干国际大学）
