A Survey on 5G Wireless Network Intrusion Detection Systems Using Machine Learning Techniques

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Author(s): Michael Chai Chon Yun (Curtin University, Malaysia)and Sivaraman Eswaran (Curtin University, Malaysia)
Copyright: 2025
Pages: 24
Source title: Digital Forensics in the Age of AI
Source Author(s)/Editor(s): Marwan Omar (Illinois Institute of Technology, USA)and Hewa Majeed Zangana (Duhok Polytechnic University, Iraq)
DOI: 10.4018/979-8-3373-0857-9.ch008

Keywords: Artificial Intelligence / Computer Science & IT / Criminal Science and Forensics / Information Science Reference

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Abstract

This survey explores the landscape of Intrusion Detection Systems (IDS) in 5G wireless networks, with a specific emphasis on those leveraging Machine Learning (ML) techniques. The deployment of 5G is poised to revolutionize telecommunications with unprecedented data rates, reduced latency, and enhanced capacity. However, the advanced infrastructure also brings heightened security challenges, necessitating robust and adaptive security measures. IDS have emerged as a key strategy to counteract these vulnerabilities, particularly those that exploit machine learning for anomaly detection and rapid response. This paper reviews several 5G IDS implementations across different domains, including the 5G Core Network, Internet of Things (IoT), and Smart Grids. Each domain's technical and cybersecurity environment is explored for better context in understanding the different implementations available, which are scrutinised based on ML models used, datasets for training and testing, and architecture.

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A Survey on 5G Wireless Network Intrusion Detection Systems Using Machine Learning Techniques

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