Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61380
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dc.contributor.authorWang, Zulien
dc.contributor.authorChiong, Raymonden
dc.contributor.authorFan, Zongwenen
dc.date.accessioned2024-07-10T01:00:31Z-
dc.date.available2024-07-10T01:00:31Z-
dc.date.issued2021-
dc.identifier.citationJournal of Intelligent and Fuzzy Systems, 41(2), p. 2551-2559en
dc.identifier.issn1875-8967en
dc.identifier.issn1064-1246en
dc.identifier.urihttps://hdl.handle.net/1959.11/61380-
dc.description.abstract<p>Malicious web domains represent a serious threat to online users' privacy and security, causing monetary loss, theft of private information, and malware attacks, among others. In recent years, machine learning methods have been widely used as prediction models to identify malicious web domains. In this study, we propose a Fuzzy-Weighted Least Squares Support Vector Machine (FW-LS-SVM) model for malicious web domain identification. In our proposed model, a fuzzyweighted operation is applied to each data sample considering the fact that different samples may have different importance. This fuzzy-weighted operation is also able to alleviate the influence of noise data and improve the model's robustness by assigning weights to error constraints. For comparison purposes, three commonly used single machine learning classifiers and three widely used ensemble models are included in our experiments, in order to assess the performance of our proposed FW-LS-SVM and its ensemble version. Hyperlink indicators and uniform resource locator-based features are used to train the prediction models. Experimental results show that our proposed approach is highly effective in identifying malicious web domains, outperforming the well-established single and ensemble models being compared.</p>en
dc.languageenen
dc.publisherIOS Pressen
dc.relation.ispartofJournal of Intelligent and Fuzzy Systemsen
dc.titleA fuzzy-weighted approach for malicious web domain identificationen
dc.typeJournal Articleen
dc.identifier.doi10.3233/JIFS-200943en
local.contributor.firstnameZulien
local.contributor.firstnameRaymonden
local.contributor.firstnameZongwenen
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.placeThe Netherlandsen
local.format.startpage2551en
local.format.endpage2559en
local.peerreviewedYesen
local.identifier.volume41en
local.identifier.issue2en
local.contributor.lastnameWangen
local.contributor.lastnameChiongen
local.contributor.lastnameFanen
dc.identifier.staffune-id:rchiongen
local.profile.orcid0000-0002-8285-1903en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:1959.11/61380en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleA fuzzy-weighted approach for malicious web domain identificationen
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.search.authorWang, Zulien
local.search.authorChiong, Raymonden
local.search.authorFan, Zongwenen
local.uneassociationNoen
dc.date.presented2021-
local.atsiresearchNoen
local.sensitive.culturalNoen
local.year.published2021en
local.year.presented2021en
local.fileurl.closedpublishedhttps://rune.une.edu.au/web/retrieve/fb81d9f4-b534-4286-990f-174259dc6e11en
local.subject.for20204602 Artificial intelligenceen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.date.moved2024-07-24en
Appears in Collections:Journal Article
School of Science and Technology
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