International Research Journal of Education and Technology

The deployment of fifth-generation (5G) wireless networks marks a transformative milestone in global communication infrastructure, enabling ultra-low latency, enhanced bandwidth, and massive connectivity for heterogeneous devices. While 5G supports revolutionary applications such as autonomous transportation, smart cities, remote healthcare, and industrial automation, it simultaneously introduces complex cybersecurity challenges. The integration of advanced technologies including Software-Defined Networking (SDN), Network Function Virtualization (NFV), Multi-access Edge Computing (MEC), and network slicing significantly expands the attack surface compared to previous generations of mobile networks. Traditional perimeter-based security mechanisms are inadequate in addressing the dynamic and virtualized architecture of 5G systems. This paper provides a comprehensive analysis of the evolving threat landscape in 5G networks by examining vulnerabilities across the radio access network, core network, virtualization layer, and edge infrastructure. Furthermore, an artificial intelligence-driven multi-layer defense framework is proposed to enhance real-time threat detection and mitigation. The framework integrates deep learning-based anomaly detection, Zero Trust Architecture principles, blockchain-enabled authentication, and secure slice isolation mechanisms. Experimental evaluation using benchmark intrusion detection datasets demonstrates superior detection accuracy and reduced false positive rates compared to conventional machine learning approaches. The study emphasizes the necessity of adaptive, intelligent, and decentralized security strategies to ensure resilience and trustworthiness in next-generation 5G ecosystems.

Keywords: 5G Security, Network Slicing, SDN, NFV, Zero Trust Architecture, Deep Learning, Intrusion Detection, Edge Computing, IoT Security.