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New hybrid framework detects crypto-ransomware with 99.64% precision

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel hybrid framework designed to detect crypto-ransomware attacks targeting enterprise shared storage. This system utilizes a Region of Interest (RoI) technique to analyze network traffic and extract Indicators of Compromise (IoCs). These IoCs enhance existing security tools, while RoI-derived features are fed into a machine learning model. The ML module demonstrates high efficacy, achieving a 99.64% detection precision with a 0% false negative rate and a 99.44% accuracy for early detection. AI

IMPACT Enhances enterprise security by providing a more effective method for detecting and preventing ransomware attacks on shared data.

RANK_REASON This is a research paper detailing a new technical framework for security, published on arXiv.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New hybrid framework detects crypto-ransomware with 99.64% precision

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Gervais Hatungimana, Abdun Naser Mahmood, Mohammad Jabed Morshed Chowdhury ·

    A Hybrid Framework For Crypto-Ransomware Detection In Enterprise Shared Storage

    arXiv:2606.30586v1 Announce Type: cross Abstract: Most corporate workplace environments enforce policies and technical controls that limit the storage of sensitive data on client endpoints. Consequently, ransomware operators have evolved variants that expand their attack surface …

  2. arXiv cs.LG TIER_1 English(EN) · Mohammad Jabed Morshed Chowdhury ·

    A Hybrid Framework For Crypto-Ransomware Detection In Enterprise Shared Storage

    Most corporate workplace environments enforce policies and technical controls that limit the storage of sensitive data on client endpoints. Consequently, ransomware operators have evolved variants that expand their attack surface from local systems to network drives and shared st…

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