Enhancing Security in 5G and Future 6G Networks: Machine Learning Approaches for Adaptive Intrusion Detection and Prevention

Konstantinos Kalodanis (), Charalampos Papapavlou and Georgios Feretzakis
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Konstantinos Kalodanis: Department of Informatics & Telematics, Harokopio University of Athens, 17778 Athens, Greece
Charalampos Papapavlou: Department of Electrical & Computer Engineering, University of Patras, 26504 Patras, Greece
Georgios Feretzakis: School of Science and Technology, Hellenic Open University, 26335 Patras, Greece

Future Internet, 2025, vol. 17, issue 7, 1-21

Abstract: The evolution from 4G to 5G—and eventually to the forthcoming 6G networks—has revolutionized wireless communications by enabling high-speed, low-latency services that support a wide range of applications, including the Internet of Things (IoT), smart cities, and critical infrastructures. However, the unique characteristics of these networks—extensive connectivity, device heterogeneity, and architectural flexibility—impose significant security challenges. This paper introduces a comprehensive framework for enhancing the security of current and emerging wireless networks by integrating state-of-the-art machine learning (ML) techniques into intrusion detection and prevention systems. It also thoroughly explores the key aspects of wireless network security, including architectural vulnerabilities in both 5G and future 6G networks, novel ML algorithms tailored to address evolving threats, privacy-preserving mechanisms, and regulatory compliance with the EU AI Act. Finally, a Wireless Intrusion Detection Algorithm (WIDA) is proposed, demonstrating promising results in improving wireless network security.

Keywords: 5G and 6G networks; ML algorithms; EU AI Act; wireless network protection (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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