Developing and Evaluating Generative AI Models for Detection and Mitigation of Security Threats in 5G Networks

The rapid advancement of technology has enhanced the connectivity and data exchange but has also introduced challenges of security threats and vulnerabilities. This study explores the development of Generative Artificial Intelligence (GAI) models to detect and mitigate 5G networks threats. The proposed framework integrates Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Large Language Models (LLMs), leveraging their unique strengths for cybersecurity. The hybrid framework achieves the superior performance with an accuracy of 97.5% and detects both known and unknown threats. Metrics such as detection accuracy, false positive rates (FPRs), computational efficiency, and robustness against the adversarial attacks are used to evaluate the system. The framework also demonstrates flexibility to adversarial threats, continuously learning, and improving threats detection and mitigation. The proposed framework of hybrid approach provides an adaptive approach to address new security challenges to the growing field of AI-driven cybersecurity.