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.

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Title Iterative construction of hierarchical classifiers for phishing website detection
Author(s) Abawajy,JORCID iD for Abawajy,J
Beliakov,GORCID iD for Beliakov,G
Chowdhury,MORCID iD for Chowdhury,M
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
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