Review of the perspective on the implementation of evolutionary algorithms in cyber security on IoT infrastructure
Year: 2025
Type: article
Abstract: 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.
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4415089075
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