Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.procs.2018.03.053
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dc.titleDetecting Phishing Websites via Aggregation Analysis of Page Layouts
dc.contributor.authorMao, J.
dc.contributor.authorBian, J.
dc.contributor.authorTian, W.
dc.contributor.authorZhu, S.
dc.contributor.authorWei, T.
dc.contributor.authorLi, A.
dc.contributor.authorLiang, Z.
dc.contributor.editorBie, R.
dc.contributor.editorSun, Y.
dc.contributor.editorYu, J.
dc.date.accessioned2021-11-16T09:30:40Z
dc.date.available2021-11-16T09:30:40Z
dc.date.issued2018
dc.identifier.citationMao, J., Bian, J., Tian, W., Zhu, S., Wei, T., Li, A., Liang, Z. (2018). Detecting Phishing Websites via Aggregation Analysis of Page Layouts. Procedia Computer Science 129 : 224-230. ScholarBank@NUS Repository. https://doi.org/10.1016/j.procs.2018.03.053
dc.identifier.issn1877-0509
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/206488
dc.description.abstractPhishing websites are typical starting points of online social engineering attacks, including many recent online scams. The attackers develop web pages mimicking legitimate websites, and send the malicious URLs to victims to lure them to input their sensitive information. Existing phishing defense mechanisms are not sufficient to detect with new phishing attacks. In this paper, we aim to improve phishing detection techniques using machine learning techniques. In particular, we propose a learning-based aggregation analysis mechanism to decide page layout similarity, which is used to detect phishing pages. Our experiment results shows that our approach is accurate and effective in detecting phishing pages. © 2018 Elsevier Ltd. All rights reserved.
dc.publisherElsevier B.V.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourceScopus OA2018
dc.subjectAggregation analysis
dc.subjectAnti-phishing
dc.subjectMachine learning
dc.subjectWeb security
dc.typeConference Paper
dc.contributor.departmentDEPARTMENT OF COMPUTER SCIENCE
dc.description.doi10.1016/j.procs.2018.03.053
dc.description.sourcetitleProcedia Computer Science
dc.description.volume129
dc.description.page224-230
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