Iterative construction of hierarchical classifiers for phishing website detection Abawajy,J, Beliakov,G, Kelarev,A and Chowdhury,M 2014, Iterative construction of hierarchical classifiers for phishing website detection, Journal of Networks, vol. 9, no. 8, pp. 2089-2098, doi: 10.4304/jnw.9.8.2089-2098. Attached Files Name Description MIMEType Size Downloads Title Iterative construction of hierarchical classifiers for phishing website detection Author(s) Abawajy,J orcid.org/0000-0001-8962-1222 Beliakov,G orcid.org/0000-0002-9841-5292 Kelarev,A Chowdhury,M orcid.org/0000-0002-2866-4955 Journal name Journal of Networks Volume number 9 Issue number 8 Start page 2089 End page 2098 Total pages 10 Publisher Academy Publisher Place of publication Oulu, Finland Publication date 2014-08 ISSN 1796-2056 Keyword(s) Ensemble classifiers Hierarchical multi-level classifiers Phishing websites Random forest Summary This article is devoted to a new iterative construction of hierarchical classifiers in SimpleCLI for the detection of phishing websites. Our new construction of hierarchical systems creates ensembles of ensembles in SimpleCLI by iteratively linking a top-level ensemble to another middle-level ensemble instead of a base classifier so that the top-level ensemble can generate a large multilevel system. This new construction makes it easy to set up and run such large systems in SimpleCLI. The present article concentrates on the investigation of performance of the iterative construction of such classifiers for the example of detection of phishing websites. We carried out systematic experiments evaluating several essential ensemble techniques as well as more recent approaches and studying their performance as parts of the iterative construction of hierarchical classifiers. The results presented here demonstrate that the iterative construction of hierarchical classifiers performed better than the base classifiers and standard ensembles. This example of application to the classification of phishing websites shows that the new iterative construction combining diverse ensemble techniques into the iterative construction of hierarchical classifiers can be applied to increase the performance in situations where data can be processed on a large computer. © 2014 ACADEMY PUBLISHER. Language eng DOI 10.4304/jnw.9.8.2089-2098 Field of Research 080108 Neural, Evolutionary and Fuzzy Computation 080501 Distributed and Grid Systems Socio Economic Objective 890199 Communication Networks and Services not elsewhere classified HERDC Research category C1 Refereed article in a scholarly journal ERA Research output type C Journal article Copyright notice ©2014, Academy Publisher Persistent URL http://hdl.handle.net/10536/DRO/DU:30069149 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 20 Jan 2015, 17:22:07 EST Tue, 20 Jan 2015, 17:24:14 EST Wed, 21 Jan 2015, 04:01:45 EST Wed, 21 Jan 2015, 05:05:26 EST Tue, 10 Feb 2015, 09:28:19 EST Tue, 10 Feb 2015, 09:33:38 EST Thu, 12 Feb 2015, 09:14:52 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 1 times in Scopus Search Google Scholar Access Statistics: 718 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Tue, 20 Jan 2015, 17:24:14 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.