Security Framework for Educational Institutions using Machine Learning: A Smart Campus Security System

Abstract

The growing adoption of sophisticated technologies in schools has created the need for strong security frameworks. In this paper, an IoT and ML-based security framework is presented to improve safety and threat detection in schools. To create an end-to-end security ecosystem, the framework incorporates anomaly detection, access control, and intelligent monitoring. Numerous security risks, such as theft, cyber attacks, and illegal access, are present in schools and institutions.

Old-fashioned security systems lack positive threat detection and real-time response. With the adoption of IoT and ML, schools can automate security through predictive analysis and real-time monitoring. A case study on the implementation of the proposed model in a university setting indicates improved threat detection, reduced response time, and improved student and teacher safety.