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crossrefFuture Internet2025-07-18Cited by 14

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

Konstantinos Kalodanis, Charalampos Papapavlou, Georgios Feretzakis

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.

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crossrefFuture Internet2023-08-14Cited by 10

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The rise in internet users has brought with it the impending threat of cybercrime as the Internet of Things (IoT) increases and the introduction of 5G technologies continues to transform our digital world. It is now essential to protect communication networks from illegal intrusi…

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crossrefFuture Internet2021-04-28Cited by 260

Designing a Network Intrusion Detection System Based on Machine Learning for Software Defined Networks

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Software-defined Networking (SDN) has recently developed and been put forward as a promising and encouraging solution for future internet architecture. Managed, the centralized and controlled network has become more flexible and visible using SDN. On the other hand, these advanta…

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crossrefFuture Internet2025-02-06Cited by 7

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crossrefFuture Internet2024-04-19Cited by 23

A Comprehensive Review of Machine Learning Approaches for Anomaly Detection in Smart Homes: Experimental Analysis and Future Directions

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Detecting anomalies in human activities is increasingly crucial today, particularly in nuclear family settings, where there may not be constant monitoring of individuals’ health, especially the elderly, during critical periods. Early anomaly detection can prevent from attack scen…

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crossrefFuture Internet2023-06-09Cited by 11

Enhancing IoT Device Security through Network Attack Data Analysis Using Machine Learning Algorithms

Ashish Koirala, Rabindra Bista, Joao C. Ferreira

The Internet of Things (IoT) shares the idea of an autonomous system responsible for transforming physical computational devices into smart ones. Contrarily, storing and operating information and maintaining its confidentiality and security is a concerning issue in the IoT. Throu…

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crossrefFuture Internet2026-02-19

A Systematic Review of Machine-Learning-Based Detection of DDoS Attacks in Software-Defined Networks

Surendren Ganeshan, R Kanesaraj Ramasamy

Software-Defined Networking (SDN) has emerged as a fundamental architecture for future Internet systems by enabling centralized control, programmability, and fine-grained traffic management. However, the logical centralization of the SDN control plane also introduces critical vul…

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