Cyber-Resilient IoT-Driven SCADA Integration for Secure and Intelligent Fuel Station Monitoring and Predictive Analysis

The paper introduces a secure, cyber-resilient IoT-SCADA convergence architecture for intelligent monitoring and control of fuel station systems. OPC UA and MQTT are utilized to provide a semantically structured supervisory control and a highly scalable telemetry system. Furthermore, deterministic field-level actuation and cloud-based analytics can be achieved. A defense-in-depth security model is implemented through OT/IT segmentation, TLS 1.3 encryption, certificate-based authentication, and machine-learning-based intrusion detection. A predictive intelligence model is developed through the implementation of a hybrid model consisting of supervised learning-based classifiers, temporal deep learning models, and a physics-informed digital twin model for detecting leaks and fraud. The proposed architecture was validated through experimentation on a representative testbed of a fuel station system consisting of four underground tanks and six dispensers. An edge gateway and a cloud-based analytics platform were utilized. Experimental results were obtained for normal operation, simulated leaks (2 L/min), meter tampering, and controlled cyberattacks. Experimental results demonstrated sub-200 ms supervisory latency, 99.97% availability, and up to 98.1% accuracy in detecting anomalies. These findings demonstrate the potential of the proposed framework to provide a highly secure and intelligent monitoring and control system for fuel station systems. This study provides a highly secure and intelligent monitoring and control system for fuel station systems. Furthermore, the proposed framework provides a highly secure and intelligent monitoring and control system for safety-critical industrial IoT applications.