A Firefly-Based Optimization Algorithm for Secure 5G-IoT Cyber-Physical Systems

The interaction between cyber-physical systems (CPS) and 5G-enabled Internet of Things (IoT) networks introduce critical challenges related to security, resource efficiency, and simulated data threat detection.Existing security mechanisms struggle to adapt to the dynamic and heterogeneous nature of these network infrastructures.To address these challenges, this study proposes a Firefly Optimization Algorithm (FOA) inspired by swarmbased firefly intelligence to enhance security, resource allocation, and energy efficiency in CPS-IoT networks.The proposed approach integrates an enhanced attraction-based motion mechanism and an adaptive mutation strategy to dynamically adjust security parameters, optimizing intrusion detection, anomaly mitigation, and encryption complexity.Empirical evaluations demonstrate that FOA outperforms existing methods in terms of detection accuracy, latency reduction, and computational efficiency, ensuring a robust and adaptive security framework for next-generation CPS systems.This research contributes to the development of intelligent, adaptive, and sustainable security solutions for 5G-enabled IoT ecosystems.