Ransomware Detection and Prevention System

Abstract

Ransomware has evolved into one of the most disruptive cyber security threats, capable of encrypting or ex filtrating user data and bypassing traditional signature-based antivirus mechanisms. Existing solutions detect attacks only after encryption begins, leading to irreversible data loss and operational downtime. This paper presents real time Ransomware detection and prevention system built on behavioral monitoring and machine learning techniques. The system continuously analyzes file operations, process activities, memory behavior, and access patterns to identify anomalies indicative of Ransomware. Machine learning models such as Random Forest and XG Boost classify suspicious behaviors with improved accuracy, enabling the detection of both known and zero-day variants. Upon identifying malicious activity, the system automatically quarantines infected files, terminates harmful processes, and alerts the user through an integrated dashboard. Additionally, automation modules enhance responsiveness by ensuring instant reaction, continuous monitoring, and reduced false positives. The proposed framework provides a proactive defense mechanism that minimizes data loss, supports early threat identification, and strengthens system resilience against emerging Ransomware families.