Host-based Intrustion Detection System Specialist Using Deep Learning

Host-based Intrustion Detection System Specialist Using Deep Learning

Mr. Salim Khan1, Jayshri Datir2, Nandkishor Jagtap3, Reshma Rajput4, Neha Sale5

*1 Assistant Professor, Department of Information Technology, Matoshri College of Engineering & Research Centre, Eklahare, Maharashtra, India

*2,3,4,5 Students, Department of Information Technology, Matoshri College of Engineering & Research Centre, Eklahare Maharashtra, India

ABSTRACT : With the increasing need for intelligent surveillance systems, our web application integrates three essential modules: motion detection, face recognition, and face mask detection. These features work together to monitor environments, identify individuals, and ensure compliance with health guidelines. The system aims to enhance security and safety across various settings by offering real-time monitoring, recognition, and alerting capabilities. This application leverages advanced algorithms such as deep learning and computer vision to improve accuracy and efficiency. It provides a scalable solution adaptable to a range of environments, from public spaces to enterprises, addressing modern challenges like identity verification and pandemic management.
Keywords: Motion Detection, Face Recognition, Face Mask Detection, Real-time Surveillance, Deep Learning, Flask Web Application, Security System, Health Compliance.