ENHANCING 5G SECURITY PRESERVATION THROUGH DYNAMIC MIXTURE OF EXPERTS AND TRANSFER LEARNING

Abstract

This paper investigates the integration of Dynamic Mixture of Experts (DMoE) and Transfer Learning (TL) for improving the security preservation of 5G. The rapid evolution of 5G brings with it challenging new issues, including enhanced attack surfaces, network slicing vulnerabilities, and risks associated with edge computing—challenges that legacy-style, counter-signature, and sandbox-based security software are simply incapable of addressing. A new method is introduced using anomaly detection, knowledge transfer, and the robustness of DMoE by leveraging the adaptive capacities of DMoE and the ability of TL to mitigate the bottleneck caused by limited data and to enhance generalization for model transfer between diverse 5G environments. This hybrid approach is designed to provide a stronger, more efficient, and more effective intrusion detection system. The study reviews existing literature, identifies key challenges, and provides a synopsis of the current state of research along with remaining questions. A research agenda and methodology are proposed to support informed implementation and assessment of such a system, focusing on practicality using accessible models and datasets. Results are based on simulations using hypothetical but realistic experiments, showing performance gains and highlighting improvements in threat awareness, reduction in false positives, and responsiveness to emerging threats—offering a proactive approach to securing dynamic 5G environments.