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Predicting Vulnerability to Malware Using Machine Learning Models: A Study on Microsoft Windows Machines.

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Title: Predicting Vulnerability to Malware Using Machine Learning Models: A Study on Microsoft Windows Machines.
Authors: Esnaashari, Marzieh1, marzie.esnaashari@atraco.ir, Moradi, Nima2, nima.moradi@mail.concordia.ca
Source: Journal of Applied Research on Industrial Engineering; Summer2025, Vol. 12 Issue 3, p541-558, 18p
Database: Applied Science & Technology Source
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DbLabel: Applied Science & Technology Source
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PubType: Academic Journal
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  Data: Predicting Vulnerability to Malware Using Machine Learning Models: A Study on Microsoft Windows Machines.
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  Data: <searchLink fieldCode="AU" term="%22Esnaashari%2C+Marzieh%22">Esnaashari, Marzieh</searchLink><relatesTo>1</relatesTo>, <i>marzie.esnaashari@atraco.ir</i><br /><searchLink fieldCode="AU" term="%22Moradi%2C+Nima%22">Moradi, Nima</searchLink><relatesTo>2</relatesTo>, <i>nima.moradi@mail.concordia.ca</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Applied+Research+on+Industrial+Engineering%22">Journal of Applied Research on Industrial Engineering</searchLink>; Summer2025, Vol. 12 Issue 3, p541-558, 18p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=190600290
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      – Type: doi
        Value: 10.22105/jarie.2025.482599.1679
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      – Code: eng
        Text: English
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        PageCount: 18
        StartPage: 541
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      – TitleFull: Predicting Vulnerability to Malware Using Machine Learning Models: A Study on Microsoft Windows Machines.
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            NameFull: Esnaashari, Marzieh
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              M: 07
              Text: Summer2025
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              Y: 2025
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