A Behavioral Intelligence Framework for Cyber Extortion Threats Detection using Behavioral Analytics & Risk Scoring

This report presents a unified system for early detection of cyber extortion activities (e.g., ransomware) using behavioral analytics, machine learning, and risk scoring. Unlike traditional signature-based approaches, the proposed system analyzes dynamic user activities and system behavior to detect anomalies in real time. The integration of anomaly detection, machine learning classification, and risk scoring improves both detection accuracy and response efficiency. The report also outlines future improvements including SIEM integration, Explainable AI (XAI), federated learning, mobile alerts, threat intelligence feeds, and cloud-native environment support. This work was conducted at Arab International University (AIU), Syria. The official website of the university is: https://www.aiu.edu.sy