AI-Driven Cyber Threat Detection and Mitigation in 5G and Beyond: Enhancing Security in the Telecom Industry - A Survey and Comparative Analysis
Kshitij Nirdhar · International Journal for Research in Applied Science and Engineering Technology · 2025
The rise of 5G and Beyond-5G (B5G) networks has revolutionized the telecom industry, enabling highspeed, low-latency communication and massive IoT connectivity. However, these advancements have introduced complex and evolving cyber threats. Traditional security systems are no longer sufficient to defend against zero-day attacks, adversarial manipulations, and sophisticated intrusions. This pa- per provides a comprehensive survey and comparative analysis of AI-driven cyber threat detection and mitigation techniques in 5G/B5G networks, covering machine learning (ML), deep learning (DL), reinforcement learning (RL), and hybrid AI approaches. A layered AIsecurity architecture is proposed, and each method is evaluated across multiple dimensions such as accuracy, scalability, real-time feasibility, and computational complexity. The study also highlights future directions, including edge AI, federated learning, explainable AI (XAI), and quantum AI, offering a roadmap for secure and intelligent next-generation networks.