Abstract: Supervisory Control and Data Acquisition (SCADA) systems are central to the efficient operation of critical infrastructure such as energy, water, and industrial networks. However, the increased digital integration of SCADA components, especially through Internet of Things (IoT) technologies, has simultaneously broadened their exposure to cyber threats. This project presents a simulated SCADA system architecture designed to model, monitor, and secure real-time industrial telemetry using open-source platforms Node-RED and ThingsBoard. Leveraging real-world data collected from the Aventa AV-7 wind turbine in Switzerland, the project implements a multilayered architecture comprising edge, fog, and cloud layers, equipped with synchronized databases for integrity comparison and threat forensics. Artificial intelligence (AI) models are integrated into the system to perform anomaly detection using supervised, unsupervised, and deep learning (LSTM) algorithms. Cyberattacks including Distributed Denial of Service (DDoS), false data injection, and replay attacks are simulated to evaluate the system’s resilience. This report details each stage of the project from data preprocessing and system design to implementation and evaluation culminating in a set of strategic recommendations for enhancing SCADA security through AI-driven frameworks.

Mohammad Alqahtani, Abdulkarim Amin, Kyounggon Kim and Seokhee Lee. “Enhancing SCADA Security in Critical Infrastructure: A Multi-Layered Architecture Using IoT-Based Monitoring and AI-Driven Anomaly Detection”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.1 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170187

@article{Alqahtani2026,
title = {Enhancing SCADA Security in Critical Infrastructure: A Multi-Layered Architecture Using IoT-Based Monitoring and AI-Driven Anomaly Detection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170187},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170187},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {1},
author = {Mohammad Alqahtani and Abdulkarim Amin and Kyounggon Kim and Seokhee Lee}
}