Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61356
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dc.contributor.authorChiong, Raymonden
dc.contributor.authorWang, Zulien
dc.contributor.authorFan, Zongwenen
dc.contributor.authorDhakal, Sandeepen
dc.date.accessioned2024-07-10T00:59:15Z-
dc.date.available2024-07-10T00:59:15Z-
dc.date.issued2022-
dc.identifier.citationExpert Systems with Applications, v.204, p. 1-10en
dc.identifier.issn1873-6793en
dc.identifier.issn0957-4174en
dc.identifier.urihttps://hdl.handle.net/1959.11/61356-
dc.description.abstract<p>Accurate identification of malicious web domains is crucial for protecting users from the risks of theft of private information, malware attack, and monetary loss. Various methods, including blacklists and machine learningbased models, have been proposed to identify malicious web domains effectively. However, maintaining an up-to-date blacklist is difficult, and standard machine learning-based models are typically sensitive to noise in data. In this paper, we propose an ensemble model based on the fuzzy-weighted Least Squares Support Vector Machine (EFW-LS-SVM) for improving malicious web domain identification. Given the fact that different data samples may have varying importance, we introduce a fuzzy-weighted operation by applying it to each data sample. This is the first time the fuzzy-weighted operation has been incorporated into an ensemble approach for malicious web domain identification. Our proposed EFW-LS-SVM delivers excellent results for identifying malicious web domains" it outperformed the compared machine learning models in terms of the F-measure score, as well as provided the best or very competitive accuracy of up to 94.50% for all datasets included in our experiments. Further, considering the imbalanced nature of benign and malicious web domain data, where malicious web domains tend to be the minority, we used the Synthetic Minority Over-sampling Technique (SMOTE) to further improve the performance of all models tested. Our experimental results confirm that SMOTE re-sampling can improve the performance of all the models, including our proposed EFW-LS-SVM—the F-measure score of EFW-LS-SVM was improved by up to 3.29%.</p>en
dc.languageenen
dc.publisherElsevier Ltden
dc.relation.ispartofExpert Systems with Applicationsen
dc.titleA fuzzy-based ensemble model for improving malicious web domain identificationen
dc.typeJournal Articleen
dc.identifier.doi10.1016/j.eswa.2022.117243en
local.contributor.firstnameRaymonden
local.contributor.firstnameZulien
local.contributor.firstnameZongwenen
local.contributor.firstnameSandeepen
local.profile.schoolSchool of Science & Technologyen
local.profile.emailrchiong@une.edu.auen
local.output.categoryC1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.publisher.placeUnited Kingdomen
local.identifier.runningnumber117243en
local.format.startpage1en
local.format.endpage10en
local.peerreviewedYesen
local.identifier.volume204en
local.contributor.lastnameChiongen
local.contributor.lastnameWangen
local.contributor.lastnameFanen
local.contributor.lastnameDhakalen
dc.identifier.staffune-id:rchiongen
local.profile.orcid0000-0002-8285-1903en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:1959.11/61356en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleA fuzzy-based ensemble model for improving malicious web domain identificationen
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.search.authorChiong, Raymonden
local.search.authorWang, Zulien
local.search.authorFan, Zongwenen
local.search.authorDhakal, Sandeepen
local.uneassociationNoen
local.atsiresearchNoen
local.sensitive.culturalNoen
local.year.published2022en
local.fileurl.closedpublishedhttps://rune.une.edu.au/web/retrieve/b8b9496a-80c5-4df8-b5d4-b3d81fd8a82den
local.subject.for20204602 Artificial intelligenceen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.date.moved2024-08-02en
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