Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Logeshwaran, J.; Dhanasekaran, S.; Sama, Mukhtar; Sati, Dayal Chandra; Kandi, Yash; Gupta, Rishi Title A name given to the resource Machine Learning-Based Intrusion Detection Systems for 5G and beyond Networks Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings - 2025 IEEE 1st International Conference on Smart Innovations in Systems, Infrastructure, Mechanical, Power, AI and Computing Technologies, SISIMPACT 2025;pp.1144-1148 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/SISIMPACT67725.2025.11439561" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/SISIMPACT67725.2025.11439561</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037449117?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037449117?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource Cyberspace; Emphasizing; Interpretability; Reinforcement; Vulnerabilities Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155787-4; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online