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.
Wireless sensor networks (WSNs) have become a critical component of modern Internet of Things (IoT) infrastructures; however, their constrained resources and distributed deployment expose them to various cyber threats. In this work, we present a machine learning-driven intrusion…
Wireless Sensor Networks (WSNs) consist of distributed sensor nodes that collect and transmit environmental data, often in resource-constrained and unsecured environments. These characteristics make WSNs highly vulnerable to various security threats. To address this, the objectiv…
Machine learning techniques for network-based intrusion detection systems (NIDS) have advanced considerably over the past decade. Still, improvements are inhibited by handcrafted feature pipelines, isolated public benchmark data, and evaluation procedures that do not reflect real…
Quantum computing introduces new computational capabilities that can support advanced cybersecurity solutions when combined with machine learning. In recent years, quantum machine learning (QML) has emerged as a promising approach for enhancing network intrusion detection systems…
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…
The rapid expansion of network environments has introduced significant cybersecurity challenges, particularly in handling high-dimensional traffic and detecting sophisticated threats. This study presents a novel, scalable Hybrid Autoencoder–Extreme Learning Machine (AE–ELM) frame…