Phishing Sites Detection from a Web Developer’s Perspective Using Machine Learning

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dc.contributor.authorZhou, Xin
dc.contributor.authorVerma, Rakesh
dc.date.accessioned2020-01-04T08:31:54Z
dc.date.available2020-01-04T08:31:54Z
dc.date.issued2020-01-07
dc.description.abstractThe Internet has enabled unprecedented communication and new technologies. Concomitantly, it has brought the bane of phishing and exacerbated vulnerabilities. In this paper, we propose a model to detect phishing webpages from a web developer’s perspective. From this standpoint, we design 120 novel features based on content from a webpage, four time-based and two search-based novel features, plus we use 34 other content-based and 11 heuristic features to optimize the model. Moreover, we select Random Committee (Base learner: Random Tree) for our framework since it has the best performance after comparing with six other algorithms: Hellinger Distance Decision Tree, SVM, Logistic Regression, J48, Naive Bayes, and Random Forest. In real-time experiments, the model achieved 99.4% precision and 98.3% MCC with 0.1% false positive rate in 5-fold crossvalidation using the realistic scenario of an unbalanced dataset.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2020.794
dc.identifier.isbn978-0-9981331-3-3
dc.identifier.urihttp://hdl.handle.net/10125/64536
dc.language.isoeng
dc.relation.ispartofProceedings of the 53rd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectMachine Learning and Cyber Threat Intelligence and Analytics
dc.subjectmachine learning
dc.subjectphishing website
dc.subjectrandom committee
dc.titlePhishing Sites Detection from a Web Developer’s Perspective Using Machine Learning
dc.typeConference Paper
dc.type.dcmiText

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