A Cybersecurity Procedure to Vulnerabilities Classification of Windows OS Based on Feature Selection and Machine Learning

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dc.contributor.authorAl-Sarray, Noor Alhuda Abdul Hasan
dc.contributor.authorDemir, Sait
dc.date.accessioned2024-09-29T15:50:53Z
dc.date.available2024-09-29T15:50:53Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description2nd International Conference on Forthcoming Networks and Sustainability in the AIoT Era (FoNeS-AIoT) -- JAN 27-29, 2024 -- Istanbul, TURKEYen_US
dc.description.abstractThe fast advancement of systems and technology has led to problems in several areas, with data protection and information security being particularly significant concerns. Cybersecurity is a recently developed field that comprises many strategies aimed at safeguarding sensitive data. By utilizing machine learning techniques, this study has improved the security of the Windows operating system. The categorization of Windows system vulnerabilities was performed using machine learning techniques such as Random Forest, Logistic Regression, Naive Bayes, K-Nearest Neighbors, and Support Vector Machine. The dataset was compiled using materials from the National Institute of Standards and Technology (NIST) and exploit-deb, Care was taken to ensure that the data is real and exists. The parameters that were assessed include accuracy, precision, recall, f1-score, and ROC AUC Score. The Random Forest approach yielded the most precise findings, with an accuracy rate of 97%. The findings indicated that the Random Forest methodology was successful in identifying vulnerabilities in security.en_US
dc.identifier.doi10.1007/978-3-031-62871-9_18
dc.identifier.endpage243en_US
dc.identifier.isbn978-3-031-62870-2
dc.identifier.isbn978-3-031-62871-9
dc.identifier.issn2367-3370
dc.identifier.issn2367-3389
dc.identifier.scopus2-s2.0-85197765876en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage229en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-031-62871-9_18
dc.identifier.urihttps://hdl.handle.net/20.500.14619/3779
dc.identifier.volume1035en_US
dc.identifier.wosWOS:001286524700018en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer International Publishing Agen_US
dc.relation.ispartofForthcoming Networks and Sustainability in the Aiot Era, Vol 1, Fones-Aiot 2024en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCybersecurityen_US
dc.subjectVulnerabilityen_US
dc.subjectFeature Selectionen_US
dc.subjectRandom Foresten_US
dc.subjectLogistic Regressionen_US
dc.subjectNaive Bayesen_US
dc.subjectK-Nearest Neighborsen_US
dc.subjectSVMen_US
dc.titleA Cybersecurity Procedure to Vulnerabilities Classification of Windows OS Based on Feature Selection and Machine Learningen_US
dc.typeConference Objecten_US

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