Next-Generation Ransomware Defense: High-Performance Computing Strategies for Monitoring Disk I/O and CPU Performance

Ransomware frequently bypasses antivirus tools, encrypting files and making data inaccessible. Traditional detection methods, which involve monitoring processes, system calls, and file activities, have high overhead and can be disrupted by sophisticated ransomware. This Researchintroduces a method for detecting ransomware on a virtual machine by collecting specific processor and disk I/O event data from the host machine and using a machine learning classifier. The random forest model excelled among seven classifiers, achieving 0.98 accuracy within 400 milliseconds across various user loads and 22 ransomware types. INDEX TERMS Deep learning, disk statistics, hardware performance counters, machine learning, ransomware, virtual machines.

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Publication Details

Journal
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Published
2024-12-23
DOI
https://doi.org/10.55041/ijsrem39917
Primary Topic
Advanced Malware Detection Techniques
Type
article
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article

Next-Generation Ransomware Defense: High-Performance Computing Strategies for Monitoring Disk I/O and CPU Performance

Deepa Rao, Manish Kumar, K. Rishitha, M. Yogeshwar Reddy
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Advanced Malware Detection Techniques
article

Next-Generation Ransomware Defense: High-Performance Computing Strategies for Monitoring Disk I/O and CPU Performance

Deepa Rao, Manish Kumar, K. Rishitha, M. Yogeshwar Reddy
article en

Abstract

Ransomware frequently bypasses antivirus tools, encrypting files and making data inaccessible. Traditional detection methods, which involve monitoring processes, system calls, and file activities, have high overhead and can be disrupted by sophisticated ransomware. This Researchintroduces a method for detecting ransomware on a virtual machine by collecting specific processor and disk I/O event data from the host machine and using a machine learning classifier. The random forest model excelled among seven classifiers, achieving 0.98 accuracy within 400 milliseconds across various user loads and 22 ransomware types. INDEX TERMS Deep learning, disk statistics, hardware performance counters, machine learning, ransomware, virtual machines.

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTVol. 08(12)
Aeronautical Development Agency (IN)
Life in Land
Openalex Percentile: Top 74%
Advanced Malware Detection Techniques
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