Adaptive intrusion detection and alert mechanism for theft in rural area using IoT and machine learning

The development of IoT devices has made modern intrusion detection techniques necessary in contemporary security systems. In this research, we propose an intelligent intrusion detection system (IIDS) that is capable of identifying violations autonomously through machine learning algorithms and IoT sensors within a closed loop ecosystem. The IIDS system incorporates IoT cameras, motion detectors, accelerometers, and ultrasonic sensors to provide multidimensional data. They are analyzed and evaluated using CNN and RNN for their algorithms. Because of system adaptability, response to urgent scenarios is prompt with minimal error in threat neutralization. This modular IIDS is suitable to different networks as it meets several IoT security needs in real-time including access control through a GSM module. Other sophisticated functionalities include detection using thermal images and integration with home automation systems.