Machine Learning-Based Intrusion Detection Systems for 5G and beyond Networks
subject: Cyberspace; Emphasizing; Interpretability; Reinforcement; Vulnerabilities
description: NextGen networks (5 G and beyond) have diversified their infrastructure. Traditional Intrusion Detection Systems (IDS) cannot effectively address the continuously evolving landscape of threats, which is why machine learning-based IDS has emerged as a crucial solution. This overview presents the trends in the application of machine learning techniques (deep learning and ensemble methods) for machine learning-based intrusion detection in 5 G and beyond networks. The important issues tackled encompass real-time anomaly detection, large-scale data processing, adaptive learning against unknown attacks, and detection outcomes. Specifically, we emphasize the promising combination of federated learning, reinforcement learning, and graph-based methods for deployment in distributed, resource-constrained network environments. We present a comprehensive overview of performance metrics such as accuracy, false positive rate, computational overhead, and scalability for each approach, highlighting the crucial trade-offs necessary for successful deployment in dynamic 5G scenarios. Furthermore, we prioritize privacy-preserving methods and secure model sharing. This abstract could further highlight that machine learning-based schemes for intrusion detection systems are important additions toward providing strong defences for cyberspace in 5 G and beyond. 2025 IEEE.
creator: Logeshwaran, J.; Dhanasekaran, S.; Sama, Mukhtar; Sati, Dayal Chandra; Kandi, Yash; Gupta, Rishi
source: Proceedings - 2025 IEEE 1st International Conference on Smart Innovations in Systems, Infrastructure, Mechanical, Power, AI and Computing Technologies, SISIMPACT 2025;pp.1144-1148
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISBN: 979-833155787-4;
language: English
type: Conference paper
coverage: Logeshwaran J., Christ University, Department of Computer Science, Karnataka, Bengaluru, India; Dhanasekaran S., Sri Eshwar College of Engineering, Department of Electronics and Communication Engineering, Tamil Nadu, Coimbatore, India; Sama M., Marwadi University, Department of Mechanical Engineering, Gujarat, Rajkot, India; Sati D.C., Apex Institute of Technology, Chandigarh University, Gharun, India; Kandi Y., Manipal University Jaipur, Department of Computer and Communication Engineering, Rajasthan, Jaipur, India; Gupta R., Manipal University Jaipur, Department of Computer Science and Engineering, Rajasthan, Jaipur, India