Ransomware Detection System Using Machine Learning and Behavioral Analysis

Ransomware remains a serious and evolving cyber threat affecting individuals, enterprises, and governments alike. This research proposes a real-time detection system that integrates behavioral analysis and machine learning to proactively detect ransomware attacks. The system combines Java for user interface and system monitoring with Python for machine learning–based anomaly detection. It monitors file system activities, process behavior, and system resource usage to identify suspicious patterns. A trained Isolation Forest model detects anomalies, and the system automatically responds by isolating potential threats. The project emphasizes early-stage detection and user alerting to minimize data loss and damage.