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AN ANALYSIS OF PHISHING CASES USING TEXT MINING

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dc.contributor.authorJang, Chaeyeon-
dc.contributor.authorLee, Ook-
dc.contributor.authorMun, Changbae-
dc.contributor.authorHa, Hyodong-
dc.date.accessioned2023-01-25T09:20:44Z-
dc.date.available2023-01-25T09:20:44Z-
dc.date.issued2022-11-
dc.identifier.issn1992-8645-
dc.identifier.issn1817-3195-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/182192-
dc.description.abstractIn the modern knowledge and information society, hacking is causing great problems in each area of the industry. Recently, techniques such as distributed denial of service attacks and attacks on management vulnerabilities of cloud servers are gradually evolving. In this study, phishing types were analyzed based on the results of word frequency analysis, clusters were identified, and network analysis was conducted. Through the graph derived from the analysis results, it was possible to identify main keywords, relationships, and trends, and present practical review items for countermeasures against phishing attacks. It also provides a foundation for designing phishing attack prevention measures. By applying this research methodology to the analysis of open source vulnerabilities in the future, it will be possible to have an adaptive defense system for changes in hacking techniques. © 2022 Little Lion Scientific.-
dc.format.extent16-
dc.language영어-
dc.language.isoENG-
dc.publisherLittle Lion Scientific-
dc.titleAN ANALYSIS OF PHISHING CASES USING TEXT MINING-
dc.typeArticle-
dc.publisher.location파키스탄-
dc.identifier.scopusid2-s2.0-85143409899-
dc.identifier.bibliographicCitationJournal of Theoretical and Applied Information Technology, v.100, no.22, pp 6758 - 6773-
dc.citation.titleJournal of Theoretical and Applied Information Technology-
dc.citation.volume100-
dc.citation.number22-
dc.citation.startPage6758-
dc.citation.endPage6773-
dc.type.docTypeArticle-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordAuthorCases Study-
dc.subject.keywordAuthorPhishing Attack-
dc.subject.keywordAuthorSemantic Network Analysis-
dc.subject.keywordAuthorText Mining-
dc.identifier.urlhttp://www.jatit.org/volumes/Vol100No22/29Vol100No22.pdf-
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