Development of a Mobile Integrated Smart Intrusion Detection System for Critical Power Assets

Abstract

Power system infrastructure such as substations and transformers is frequently targeted by criminals and vandals due to the high value of copper cables and other equipment. Existing security measures often fail due to slow response times, false alarms, and limited monitoring capabilities. To address this menace, a mobile-integrated smart intrusion detection framework is designed to safeguard critical power infrastructure against the growing threats of physical and cyber intrusions, vandalism, energy theft, and sabotage. This framework combines advanced sensing technologies and artificial intelligence (AI) to provide real-time, comprehensive, and adaptive protection. At its core, the system utilizes a Raspberry Pi microprocessor as the central hub for data acquisition, processing, and communication. The framework leverages AI-based image recognition to ensure high detection accuracy while minimizing false positives for anomaly detection between rodents and humans. The system's mobile integration enables real-time alerts and remote management through a Telegram-based bot, empowering security personnel to respond swiftly to security threats. This approach enhances the security posture and resilience of critical power infrastructure against evolving threats. This framework offers a scalable, cost-effective, and intelligent solution, making it suitable for widespread adoption. By providing real-time monitoring and high detection accuracy, this framework enables security personnel to respond quickly to security breaches, protecting critical power assets from various threats.