AI-Powered Cybersecurity for Critical Infrastructure: A Comprehensive Survey

Abstract

The rise in digital dependency has made critical infrastructure systems prime targets for sophisticated cyberattacks. Artificial Intelligence (AI), with its ability to analyze large datasets, detect anomalies, and respond to evolving threats, offers a promising path forward in cybersecurity. This paper presents a comprehensive survey of the application of AI techniques in securing critical infrastructure, including power grids, water systems, and transportation networks. The study reviews key AI models, identifies current challenges, and discusses future directions for enhancing cyber resilience.

Introduction

Critical infrastructure systems like power grids, transportation, water supply, and healthcare increasingly rely on interconnected digital networks, making them vulnerable to sophisticated cyber threats. Traditional cybersecurity methods often struggle to keep pace with these evolving risks. Artificial Intelligence (AI) has emerged as a powerful tool to enhance cybersecurity by enabling proactive threat detection, real-time responses, and smarter decision-making.

This paper surveys AI techniques such as machine learning, deep learning, natural language processing, and reinforcement learning, highlighting their applications in protecting critical infrastructure. For example, AI detects anomalies in smart energy grids, monitors water system parameters to prevent contamination, secures transportation networks against cyberattacks, and safeguards healthcare data from breaches and ransomware.

Despite its promise, AI faces challenges like the need for large, high-quality data, vulnerability to adversarial attacks, lack of interpretability, and ethical/legal concerns related to autonomous decision-making. The paper suggests future directions including federated learning for privacy-preserving AI, explainable AI for transparency, edge computing for real-time protection, and enhanced collaboration between AI systems and human experts to strengthen cybersecurity in critical infrastructure.

Conclusion

AI stands as a transformative force in protecting critical infrastructure from evolving cyber threats. This survey has outlined the major AI techniques, their applications, and challenges, with a focus on future advancements. As AI models become more robust and interpretable, their role in national security and public safety infrastructure will only deepen.

References

[1] S. Bhunia et al., “Hardware security: A tutorial on emerging mechanisms and applications,” IEEE Design & Test, vol. 33, no. 5, pp. 52-67, 2016. [2] M. M. Rathore et al., “A review of AI techniques for cybersecurity and critical infrastructure,” Future Generation Computer Systems, vol. 99, pp. 682–705, 2019. [3] T. Nguyen et al., “Machine learning and deep learning frameworks and libraries for large-scale data mining: A survey,” Artificial Intelligence Review, vol. 52, no. 1, pp. 77–124, 2019. [4] R. Mitchell and I. R. Chen, “A survey of intrusion detection techniques for cyber-physical systems,” ACM Computing Surveys, vol. 46, no. 4, pp. 1–29, 2014.

Copyright

Copyright © 2025 Urmila M M. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.