Insider Threat Detection Using Machine Learning

Abstract

Insider threats represent one of the most significant cybersecurity challenges in modern organizations. These threats originate from individuals within the organization who have authorized access to sensitive systems and data. This research paper presents an intelligent system for Insider Threat Detection using Machine Learning (ML) techniques. The system employs user behavior analytics, real-time log monitoring, and anomaly detection to identify suspicious activities. The proposed framework integrates a Flask-based web interface with a backend SQLite database and leverages scikit-learn for anomaly detection. The model effectively detects unauthorized access, abnormal data transfer, and unusual system usage patterns. The paper discusses methodology, implementation, challenges, and future enhancements involving deep learning and blockchain-based security measures.