---

DEVELOPMENT OF SMART INTRUSION DETECTION AND ALARM SYSTEMS TO CURB VANDALIZATION OF INFRASTRUCTURE IN THE POLYTECHNIC ADMINISTRATIVE BUILDING

Babalola, A.D, Olufemi, T.O. (Ph.D)

---

Download PDF

---

Abstract

This study investigates the development of an intelligent intrusion detection and alarm system to reduce infrastructure vandalism at the Polytechnic Administrative Building. This article emphasizes the critical need for improved security protocols in educational settings and proposes an innovative strategy that combines Internet of Things (IoT), machine learning, and blockchain technology. The system's frame work integrates the indoor and outdoor monitoring features, such as a 4k high-definition camera with an intelligent facial recognition detector and device with infrared sensor for unauthorized intruders. The main control systems analyze the images received from different camera locations on each floor and use machine learning techniques to identify and detect anomalies and potential security breaches and violations. The research also includes anti-tamper devices to protect the installed equipment. The analysis reveals that 10% of the data points fall into the category of anomalies, requiring critical attention. The analysis reveals a slight negative correlation between average and maximum motion, thereby unveiling intricate patterns in potential intrusion scenarios. This comprehensive campus security policy aims to minimize theft and damage while exhibiting the institution's commitment to using innovative technology for the safety and well-being of its community. The proposed approach offers a scalable and adaptive answer to evolving security problems in educational settings.

Keywords: Smart Intrusion Detection, IoT Security Systems, Machine Learning Algorithms, Vandalism Prevention, Blockchain Technology, Real-time Monitoring, Data Visualization in Security