A Comprehensive Analysis of Bio-Inspired Intelligent Mechanisms for Data Communication Security in Cyber-Physical Systems

Cyber-Physical Systems (CPS) face escalating security challenges as they become increasingly integral to critical infrastructure. This paper presents a comprehensive analysis of bio-inspired intelligent mechanisms for enhancing data communication security in CPS, evaluating 18 recent studies across five key paradigms: swarm intelligence, artificial immune systems, evolutionary algorithms, neural networks, and hybrid models. Our systematic review reveals that nature-inspired approaches achieve superior threat detection and energy efficiency compared to traditional methods. However, face significant implementation challenges including simulation-reality disparities (80% studies lacking hardware validation), scalability limitations (only 27% testing more than 1,000 nodes), and computational overhead (21-63% latency increase). The paper provides a taxonomy of bio-inspired security mechanisms with performance benchmarks. Identify critical gaps in physical-layer protection and real-world deployment. This research work provides recommendations for hybrid model development and standardized evaluation. Findings demonstrate that combining swarm intelligence’s adaptability with immune systems’ robustness offers the most promising path forward, while highlighting the urgent need for hardware-validated solutions in industrial IoT and smart grid applications. This research provides both a state-of-the-art reference and actionable insights for designing next-generation CPS security architectures that balance resilience with operational constraints.