International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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HYDRAGUARD: RANSOMWARE DETECTION TOOL

Author(s) Ms. AYISHA KHANUM, Mr. Prashant Nellor, Ms. Prathibha J Mirajkar, Ms. Rachana M M, Mr. Rajath Ravikumar
Country India
Abstract The increasing frequency and sophistication of ransomware attacks have created an urgent need for intelligent and proactive cybersecurity solutions. HydraGuard, a Machine-Learning Based Ransomware Detection Tool, aims to identify malicious file behavior, encryption attempts, and unauthorized system changes in real-time. The system combines file-behavior analytics, anomaly detection models, and signature-independent ML classifiers to detect ransomware before it encrypts large volumes of data. HydraGuard monitors file I/O patterns, entropy shifts, renaming bursts, and suspicious extension changes while predicting threats using a trained Random Forest–based classification model.
The platform is designed with three portals: an Admin Portal for threat monitoring and logs, a Security Analyst Portal for ML insights and attack patterns, and a System Monitor Module for real-time file watching. By integrating ML models, automated alerts, and secure system monitoring, HydraGuard significantly reduces detection time, improves response accuracy, and provides a lightweight yet effective defense against modern ransomware families.
Keywords Ransomware detection, Malware analysis, Machine learning, File monitoring, Entropy analysis, Threat detection.
Field Engineering
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-12-11
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.62736
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E-ISSN 2582-2160
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