Paper Contents
Abstract
This project monitors network activity and data access in real-time using machine learning to detect unauthorized access. It improves anomaly detection with adaptive algorithms that reduce false positives. The system integrates with automated incident response tools for quick action and supports compliance through detailed logs. Future upgrades aim to predict breaches by analyzing user behavior trends, enhancing proactive security.
Copyright
Copyright © 2025 Kavya S. This is an open access article distributed under the Creative Commons Attribution License.
Paper ID:
IJPREMS50400011813
ISSN:
2321-9653
Publisher:
ijprems