Deep Learning Techniques for Cybersecurity in Critical Infrastructure

Authors

  • Priyali Mandal Master of Computer Applications (MCA) Program, Amity Institute of Information and Tech- nology, Amity University, India Gurugram, Manesar, Panchgaon, Haryana
  • Sarita Assistant Professor Amity School of Engineering &Technology Amity University , Gurugram (Haryana), India
  • Amar Saraswat Assistant Professor, School of Engineering & Technology - Dept. of CSE K. R. Mangalam University, Gurugram
  • Dr Megha Rana Associate Professor , School of Computer Science & Engineering, IILM University Gurgaon, India

Keywords:

AI, Cybersecurity, Critical Infrastructure, Machine Learning, Deep Learning.

Abstract

The part of AI-powered cybersecurity is an amazing solution for securing complex infrastructure. The cure for augmenting or increasing the resilience of infrastructure such as power grids, water treatment plants, and transportation systems includes automated threat detection and incident response, along with prevention by AI. Moreover, AI-based threat hunting predicts unseen threats to help security teams prevent risk events from developing into major issues. Yet, significant challenges such as data quality, model biases, explanations, and skills shortages must be overcome to unlock the full potential of AI in cybersecurity. Finding the balance between a lively electronic future for critical infrastructure and its protection requires her to spur more research and development in adversarial machine learning.

DOI: https://doi.org/10.24321/3117.4787.202603

 

References

Riggs, Hugo, et al. “Impact, vulnerabilities, and mitigation strategies for cyber-secure criti- cal infrastructure.” Sensors 23.8 (2023).

Neto, Euclides Carlos Pinto, et al. “CICIoT2023: A realtime dataset and benchmark for large-scale attacks in IoT environment.” Sensors 23.13 (2023).

Haleem, Abid, et al. “Understanding the role of digital technologies in education: A review.” Sustainable operations and computers 3 (2022).

Published

2026-05-14