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dc.contributor.authorNaser Jaber, Aws
dc.contributor.authorFritsch, Lothar
dc.contributor.authorHaugerud, Hårek
dc.date.accessioned2023-02-01T15:11:53Z
dc.date.available2023-02-01T15:11:53Z
dc.date.created2022-05-25T09:48:04Z
dc.date.issued2022-04-11
dc.identifier.isbn9781665409353
dc.identifier.isbn978-1-6654-0934-6
dc.identifier.issn2767-7699
dc.identifier.issn2574-1403
dc.identifier.urihttps://hdl.handle.net/11250/3047807
dc.description.abstractWith the recent epidemic of COVID-19-themed scam and phishing, the efficient automated detection of such attacks is crucial. Although many anti-phishing solutions, such as lists and similarity and heuristic-based approaches detect attacks, methods still can be improved. Classification accuracy is highly dependent on the feature selection method used to select appropriate features for classification. In this article, a multi-objective grey wolf optimizer is used to select proper features for classifying phishing websites through a variational autoencoder. Our results indicate the superiority of the classification rate compared with related work: A classification rate of 97.49%, is obtained, thereby suggesting the feasibility of evaluating our work.en_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.ispartof2022 International Conference on Electronics, Information, and Communication (ICEIC 2022)
dc.relation.ispartofseriesInternational Conference on Electronics, Information, and Communication (ICEIC);2022 International Conference on Electronics, Information, and Communication (ICEIC)
dc.relation.urihttps://ieeexplore.ieee.org/document/9748592
dc.subjectCybersecurityen_US
dc.subjectPhishing website detectionen_US
dc.subjectAntiphishingen_US
dc.subjectMachine learningen_US
dc.titleImproving Phishing Detection with the Grey Wolf Optimizeren_US
dc.typeConference objecten_US
dc.description.versionacceptedVersionen_US
cristin.ispublishedtrue
cristin.fulltextoriginal
dc.identifier.doihttps://doi.org/10.1109/ICEIC54506.2022.9748592
dc.identifier.cristin2027213
dc.source.volume7en_US
dc.source.issue7en_US
dc.source.pagenumber6en_US


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