A Survey on 5G Wireless Network Intrusion Detection Systems Using Machine Learning Techniques
Michael Chai Chon Yun, Sivaraman Eswaran · Advances in digital crime, forensics, and cyber terrorism book series · 2024
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.