AI-driven predictive analytics for proactive security and optimization in critical infrastructure systems
1 M & M Technical Services, Nigeria.
2 Babcock University, Ilishan-Remo, Ogun State, Nigeria.
3 Independent Researcher, Canada.
4 Independent Researcher, Lagos Nigeria.
5 Amstek Nigeria Limited, Nigeria
6 Independent Researcher, Nigeria.
Review
Open Access Research Journal of Science and Technology, 2021, 02(02), 006-015.
Article DOI: 10.53022/oarjst.2021.2.2.0059
Publication history:
Received on 28 August 2021; revised on 21 October 2021; accepted on 25 October 2021
Abstract:
Critical infrastructure systems, such as energy grids, transportation networks, and healthcare facilities, form the backbone of modern society, necessitating robust security and optimization measures. Traditional approaches to managing these systems often struggle to address the dynamic challenges posed by evolving threats and inefficiencies. This paper explores the transformative potential of AI-driven predictive analytics in enhancing proactive security and system optimization. By leveraging advanced technologies such as machine learning and neural networks, predictive analytics enables the identification and mitigation of potential threats, as well as the optimization of resource allocation and operational efficiency. Theoretical foundations, practical applications, and challenges related to the integration of AI into critical infrastructure systems are discussed in detail. The paper concludes with actionable recommendations for implementing AI solutions, emphasizing data infrastructure, cybersecurity, cross-sector collaboration, and ethical governance. These insights aim to provide a roadmap for leveraging AI to create resilient, efficient, and secure critical infrastructure systems in the face of emerging global challenges.
Keywords:
AI-driven predictive analytics; Critical infrastructure security; System optimization; Machine learning applications; Proactive threat mitigation
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Copyright © 2021 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0