AI-driven Algorithms for Real-time Surveillance of Critical Infrastructure

Authors: Ravikanth Konda

DOI: https://doi.org/10.37082/IJIRMPS.v10.i1.232457

Short DOI: https://doi.org/g9hm89

Country: USA

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Abstract: The increasing threat of physical and cyberattacks against critical infrastructure has resulted in the demand for intelligent, real-time surveillance solutions. Artificial Intelligence (AI) offers a revolutionary solution to monitoring complex environments like power grids, transportation centers, and water treatment plants by allowing automated threat detection, anomaly analysis, and predictive security actions. This paper explores the use of AI-based algorithms in real-time surveillance systems for critical infrastructure. Through the combination of machine learning (ML), deep learning (DL), and computer vision methods, AI systems improve situational awareness and minimize response times. This paper discusses the state of the art in AI for infrastructure surveillance, offers a taxonomy of applicable algorithms, and examines their performance in operational deployments. We also introduce an edge and cloud computing-based modular AI-based surveillance framework for scale and security in monitoring. Our results indicate that with ethical considerations and regulation in place, AI can have a dramatic impact on the resilience and robustness of critical infrastructure monitoring. Furthermore, this paper addresses challenges, data management, legacy system integration, and interoperability. Particular emphasis is placed on real-world deployments, comparison of AI models, and a future development roadmap.

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Paper Id: 232457

Published On: 2022-01-07

Published In: Volume 10, Issue 1, January-February 2022

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