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Stream/Area:
-
Subject:
-
Paper ID:
10.55041/IJSREM39917
Volume:
8 |
12 |
Dec 2024
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
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.
KEYWORDS
Publication Date
12/21/2024
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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