Adaptive Cybersecurity Risk Assessment and Mitigation for Secure 5G Communication Networks

Abstract

Emerging technologies like smart cities, smart grids, automated factories and systems, intelligent transportation systems and massive Internet of Things (IoT) provide significant advantages to mankind. However, the heterogeneous and highly dynamic nature of 5G architectures introduces significant cybersecurity challenges like security, privacy and availability of service etc. Heterogeneity and hyper dynamism are essential characteristics of next-generation wireless communication network (5G) and they bring numerous security issues which could badly affect its reliability. In this work, we present an efficient solution to reduce cybersecurity risks in wireless communication networks by fusing Software-Defined Networking and Network Function Virtualization (SDN/NFV) enabled infrastructure with AI-based security paradigm. The framework combines a rule-based expert system and fuzzy logic for adaptive threat detection and risk assessment, while Dijkstra, AODV, and Genetic Algorithm-based routing techniques are employed to ensure secure and efficient data transmission. These methods handle vague uncertainties existing in networks effectively and identify safest path with respect to dynamic security and QoS parameters. Simulation results prove better performance with a detection rate of 98.9%, Packet Delivery Ratio (PDR) of 99.4% and latency as low as 22 ms.