Phishing detection : a case analysis on classifiers with rules using machine learning

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dc.contributor.authorThabtah, Fadi
dc.contributor.authorKamalov, Firuz
dc.date.accessioned2020-02-13T09:08:39Z
dc.date.available2020-02-13T09:08:39Z
dc.date.copyright2017en_US
dc.date.issued2017-12-01
dc.descriptionThis review is not available at CUD collection. The version of scholarly record of this review is published in Journal of Information and Knowledge Management (2017), available online at: https://doi.org/10.1142/S0219649217500344.en_US
dc.description.abstractA typical predictive approach in data mining that produces If-Then knowledge for decision making is rule-based classification. Rule-based classification includes a large number of algorithms that fall under the categories of covering, greedy, rule induction, and associative classification. These approaches have shown promising results due to the simplicity of the models generated and the user's ability to understand, and maintain them. Phishing is one of the emergent online threats in web security domains that necessitates anti-phishing models with rules so users can easily differentiate among website types. This paper critically analyses recent research studies on the use of predictive models with rules for phishing detection, and evaluates the applicability of these approaches on phishing. To accomplish our task, we experimentally evaluate four different rule-based classifiers that belong to greedy, associative classification and rule induction approaches on real phishing datasets and with respect to different evaluation measures. Moreover, we assess the classifiers derived and contrast them with known classic classification algorithms including Bayes Net and Simple Logistics. The aim of the comparison is to determine the pros and cons of predictive models with rules and reveal their actual performance when it comes to detecting phishing activities. The results clearly showed that eDRI, a recently greedy algorithm, not only generates useful models but these are also highly competitive with respect to predictive accuracy as well as runtime when they are employed as anti-phishing tools. © 2017 World Scientific Publishing Co.en_US
dc.identifier.citationThabtah, F., & Kamalov, F. (2017). Phishing detection: A case analysis on classifiers with rules using machine learning. Journal of Information and Knowledge Management, 16(4). https://doi.org/10.1142/S0219649217500344en_US
dc.identifier.issn02196492
dc.identifier.urihttp://dx.doi.org/10.1142/S0219649217500344
dc.identifier.urihttp://hdl.handle.net/20.500.12519/137
dc.language.isoenen_US
dc.publisherWorld Scientific Publishing Co. Pte Ltden_US
dc.relationAuthors Affiliations: Thabtah, F., Nelson Marlborough Institute of Technology, Auckland, New Zealand; Kamalov, F., Canadian University of Dubai, Dubai, United Arab Emirates
dc.relation.ispartofseriesJournal of Information and Knowledge Management;Vol. 16, no. 4
dc.rightsPermission to reuse the abstract has been secured from World Scientific Publishing Co. Pte Ltd.
dc.rights.holderCopyright : 2017 World Scientific Publishing Co.
dc.subjectClassificationen_US
dc.subjectData miningen_US
dc.subjectMachine learningen_US
dc.subjectPhishingen_US
dc.subjectRule-based classifiersen_US
dc.subjectRulesen_US
dc.subjectWebsite securityen_US
dc.titlePhishing detection : a case analysis on classifiers with rules using machine learningen_US
dc.typeReviewen_US

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