Ransomware Detection System Using Machine Learning and Behavioral Analysis
Year: 2025
Type: article
Abstract: 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.
Author: Shivam Dombe, Chandan Mistry, Simran Bhatnagar
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6911950872
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