AI And Machine Learning-Based Border Intrusion Detection System

Border areas require continuous monitoring to identify unauthorized movement and potential intrusion events.Conventional border monitoring systems generally depend on security personnel, cameras, or individual sensors, which can result in delayed detection and false alarms.This paper presents an Artificial Intelligence and Machine Learning-Based Border Intrusion Detection System that combines multiple sensors, wireless communication, machine learning, and camera-based monitoring.The proposed system uses a Passive Infrared sensor to detect movement, an ultrasonic sensor to measure distance, and a vibration sensor to detect physical disturbance.An ESP32 microcontroller collects sensor readings and provides them to a Random Forest classification model for identifying normal and intrusion conditions.An ESP32-CAM is used for visual monitoring of the protected area.Long Range communication is used for wireless transmission of detected event information, while an OLED display, red and green light-emitting diodes, and a buzzer provide system status and alerts.The proposed system provides an automated and lowcost approach to border intrusion monitoring by combining information from multiple sensors with machine learning-based classification and visual verification.