Intrusion Prevention System for SCADA Systems using Advanced and Latest AI Techniques

DOI

10.2991/978-94-6239-616-6_105How to use a DOI?

Keywords

SCADA; Intrusion Prevention System; Artificial Intelligence; Machine Learning; Deep Learning; Anomaly Detection; Cyber-attacks; Critical Infrastructure

Abstract

The increasing reliance on Supervisory Control and Data Acquisition (SCADA) structures for handling important infrastructure makes them prone to cyber-assaults, posing significant dangers to operational security. To deal with this, a sophisticated Intrusion Prevention System (IPS) the use of Artificial Intelligence (AI) techniques is proposed to beautify the safety of SCADA structures. By incorporating device mastering (ML), deep gaining knowledge of, and anomaly detection, this IPS gives actual-time chance detection and prevention skills, safeguarding SCADA structures from a huge range of capacity intrusions. The AI-driven IPS continuously learns from incoming statistics, permitting it to evolve to evolving cyber threats, making sure that new attack styles are quick recognized and mitigated. Key strategies inclusive of supervised and unsupervised learning algorithms are hired to classify and find malicious sports activities, whilst deep learning fashions offer a robust technique to figuring out complicated, formerly unseen attack vectors. The effectiveness of AI in SCADA systems is examined via more potent accuracy and reduced false positives, drastically enhancing device resilience. This method now not fine strengthens the safety of SCADA networks but additionally guarantees the integrity of the critical infrastructure they manual. By implementing AI-based IPS, companies can proactively shield against cyber threats, decreasing dangers and making sure clean operations in business environments.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - T. John Sunder Singh
AU  - J. I. Sheeba
AU  - S. Pradeep Devaneyan
PY  - 2026
DA  - 2026/03/31
TI  - Intrusion Prevention System for SCADA Systems using Advanced and Latest AI Techniques
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 1441
EP  - 1474
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_105
DO  - 10.2991/978-94-6239-616-6_105
ID  - Singh2026
ER  -