Attacks Detection in IoT-enabled 5G and beyond Networks: Performance Evaluation of Integrating Cutting-Edge Technologies
Zahraa Ghabriess, Hassan Harb, Ali Mansour, Koffi Clément Yao, Christophe Osswald · 2025
Recently, the use of Internet of Things (IoT) with 5G and beyond networks had led to a revolution in telecommunication systems. It improves device interconnectivity and ensures real-time data exchange, allowing for predictive maintenance, efficient resource management, and enhanced user experiences. However, such revolution also drives the emergence of sophisticated cyberattacks, outpacing traditional defense mechanisms and demanding more advanced security solutions. In order to detect such emerging threats, researchers have focused on cutting-edge technologies to strength the security and maintain the performance of IoT-enabled 5G and beyond networks. This paper presents a comparative study of recent advances on attack detection in IoT-enabled 5G and beyond networks, focusing on the integration of Artificial Intelligence (AI), Federated Learning (FL), and Edge Computing (EC) technologies. Based on specific criteria, recent works were selected and implemented, followed by a Friedman test to identify the most effective approach. The paper also highlights the key challenges in designing a secure IoT-enabled 5G networks in future work, including resource restriction, data and network heterogeneity, system expandability and adaptability, and emerging security and privacy threats.