Review of the perspective on the implementation of evolutionary algorithms in cyber security on IoT infrastructure
Jesús Enrique Soto-Soto, José-Isidro Hernández-Vega, Alejandra Guadalupe Silva Trujillo, Luis Alejandro Reynoso-Guajardo, Carlos Hernández-Santos, Mario Carlos Gallardo-Morales · International Journal of Combinatorial Optimization Problems and Informatics. · 2025
The rapid growth of the Internet of Things (IoT) in industrial environments has increased efficiency but also heightened vulnerability to sophisticated cyber-attacks. Traditional cyber security approaches are insufficient to protect critical infrastructure, creating a need for dynamic, adaptive solutions. Evolutionary algorithms (EAs), owing to their ability to explore large search spaces and optimise parameters, offer a promising route to enhancing IoT security. This review highlights the integration of EAs with deep-learning techniques to improve intrusion detection and system resilience. Building on this background, we propose an adaptive cyber-security framework that leverages evolutionary optimisation and continual learning to detect, prevent and mitigate attacks in real time. The study emphasises the importance of validating hybrid models in real-world settings and of optimising computational efficiency. Future work should investigate autonomous response mechanisms and the scalability of solutions for large-scale Industrial IoT (IIoT) deployments, ensuring robust protection against emerging threats and aligning academic advances with industry needs.