International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Ransomware has become one of the most destructive and financially damaging cyber threats, capable of encrypting critical data and demanding ransom payments for recovery. Traditional antivirus solutions based on static signatures are often ineffective against newly emerging or polymorphic ransomware variants. To address these limitations, this research presents a Machine Learning (ML)–based Ransomware Detection and Mitigation Framework implemented using Python.
The proposed system performs behavioral analysis of file operations and process activities, monitoring parameters such as file-write frequency, entropy levels, extension modifications, CPU utilization, and process lineage. These behavioral features are used to train a Random Forest classifier that distinguishes between normal user operations and ransomware-like activity. The trained model, integrated with Python’s Watchdog library, continuously monitors the file system in real time and automatically quarantines or isolates suspicious files upon detection.
Additionally, the framework incorporates backup and recovery mechanisms that periodically create immutable file snapshots, ensuring data integrity and supporting post-attack restoration. The combination of Python’s data processing ecosystem and ML algorithms provides a scalable, adaptive, and proactive defense mechanism against evolving ransomware threats.
Experimental evaluations demonstrate that the model effectively detects ransomware-like behavior with high accuracy and minimal false positives. Overall, this work contributes to a practical, intelligent, and automated cybersecurity framework for ransomware prevention, early detection, and mitigation, thereby reducing potential data loss and enhancing system resilience against modern cyberattacks.

Keywords: Ransomware, Machine Learning, Cybersecurity, Python, Random Forest, File Behavior Analysis, Data Protection, Threat Mitigation.

Downloads: | DOI: 10.17148/IJARCCE.2025.141104