RansomGuard AI: An AI-Powered Ransomware Detection and Storage Protection System

Ransomware continues to pose a severe threat to data availability by executing unauthorized encryption, rapid file alterations, and malicious extension changes across local file storage.This paper introduces RansomGuard AI, an integrated defense-in-depth storage surveillance and recovery system designed to monitor filesystem events, analyze behavioral risk indicators, and maintain cryptographically verified snapshot recovery points.Operating on a Python FastAPI backend paired with a reactive web dashboard, the framework continuously evaluates filesystem activity against key telemetry signals: modification rates, burst write frequencies, Shannon entropy shifts, and file extension mutations.To guarantee disaster recovery without relying on compromised storage, recovery copies are secured using immutable SHA-256 manifest checkpoints.The system includes a sandboxed synthetic attack simulator that reproduces ransomwarelike behaviors inside an isolated environment, demonstrating complete monitoring, detection, audit logging, and pointin-time recovery workflows without exposing the host OS to live malware.

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Publication Details

Journal
IJARCCE
Published
2026-10-28
DOI
https://doi.org/10.17148/ijarcce.2026.1510120
Primary Topic
Advanced Malware Detection Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

RansomGuard AI: An AI-Powered Ransomware Detection and Storage Protection System

Rithvik Rajmohan, P. Durgadevi, Bharath R, Roheith R
3 links
IJARCCE
Advanced Malware Detection Techniques
article

RansomGuard AI: An AI-Powered Ransomware Detection and Storage Protection System

Rithvik Rajmohan, P. Durgadevi, Bharath R, Roheith R
article en

Abstract

Ransomware continues to pose a severe threat to data availability by executing unauthorized encryption, rapid file alterations, and malicious extension changes across local file storage.This paper introduces RansomGuard AI, an integrated defense-in-depth storage surveillance and recovery system designed to monitor filesystem events, analyze behavioral risk indicators, and maintain cryptographically verified snapshot recovery points.Operating on a Python FastAPI backend paired with a reactive web dashboard, the framework continuously evaluates filesystem activity against key telemetry signals: modification rates, burst write frequencies, Shannon entropy shifts, and file extension mutations.To guarantee disaster recovery without relying on compromised storage, recovery copies are secured using immutable SHA-256 manifest checkpoints.The system includes a sandboxed synthetic attack simulator that reproduces ransomwarelike behaviors inside an isolated environment, demonstrating complete monitoring, detection, audit logging, and pointin-time recovery workflows without exposing the host OS to live malware.

IJARCCEVol. 15(10)
SRM Institute of Science and Technology (IN)
Openalex Percentile: Top 13%
Advanced Malware Detection Techniques
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