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

dc.contributor.author Thabtah, Fadi
dc.contributor.author Kamalov, Firuz
dc.date.accessioned 2020-02-13T09:08:39Z
dc.date.available 2020-02-13T09:08:39Z
dc.date.copyright 2017 en_US
dc.date.issued 2017-12-01
dc.description This 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.abstract A 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.citation Thabtah, 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/S0219649217500344 en_US
dc.identifier.issn 02196492
dc.identifier.uri http://dx.doi.org/10.1142/S0219649217500344
dc.identifier.uri http://hdl.handle.net/20.500.12519/137
dc.language.iso en en_US
dc.publisher World Scientific Publishing Co. Pte Ltd en_US
dc.relation Authors Affiliations: Thabtah, F., Nelson Marlborough Institute of Technology, Auckland, New Zealand; Kamalov, F., Canadian University of Dubai, Dubai, United Arab Emirates
dc.relation.ispartofseries Journal of Information and Knowledge Management;Vol. 16, no. 4
dc.rights Permission to reuse the abstract has been secured from World Scientific Publishing Co. Pte Ltd.
dc.rights.holder Copyright : 2017 World Scientific Publishing Co.
dc.subject Classification en_US
dc.subject Data mining en_US
dc.subject Machine learning en_US
dc.subject Phishing en_US
dc.subject Rule-based classifiers en_US
dc.subject Rules en_US
dc.subject Website security en_US
dc.title Phishing detection : a case analysis on classifiers with rules using machine learning en_US
dc.type Review en_US
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