A Review of Attack-Proof Pressure Sensor Models for Oil-Pipeline SCADA: Intelligent Modeling, Electronics Integration, and Real-World Applications

Abstract

The increasing reliance on Supervisory Control and Data Acquisition (SCADA) systems in oil pipeline infrastructures has exposed critical vulnerabilities, particularly in pressure sensor subsystems that serve as primary indicators of pipeline integrity. Cyber-physical attacks targeting these sensors can lead to catastrophic failures, economic losses, and environmental hazards. This paper presents a comprehensive review of attack-proof pressure sensor models tailored for oil-pipeline SCADA systems, emphasizing intelligent modeling techniques, secure electronics integration, and real-world deployment strategies. The study explores the convergence of cryptographic principles, chaotic system-based modeling, and generative artificial intelligence for enhancing sensor resilience against spoofing, replay, and data injection attacks. Various methodologies, including chaos-based encryption, anomaly detection via machine learning, and hardware-level security enhancements, are critically examined. The findings reveal a shift from traditional threshold-based monitoring toward adaptive, self-healing sensor frameworks capable of real-time threat mitigation. This review contributes by synthesizing interdisciplinary advancements, identifying research gaps in secure sensor design, and proposing future directions for integrating AI-driven cryptographic mechanisms within SCADA ecosystems.