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Performance evaluation of multi-tier ensemble classifiers for phishing websites

Abawajy, Jemal, Beliakov, Gleb, Kelarev, Andrei and Yearwood, John 2012, Performance evaluation of multi-tier ensemble classifiers for phishing websites, in ATIS 2012 : Proceedings of the 3rd Applications and Technologies in Information Security Workshop, School of Information Systems, Deakin University, Melbourne, Vic., pp. 11-16.

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Title Performance evaluation of multi-tier ensemble classifiers for phishing websites
Author(s) Abawajy, Jemal
Beliakov, Gleb
Kelarev, Andrei
Yearwood, John
Conference name Applications and Technologies in Information Security. Workshop (3rd : 2012 : Melbourne, Vic.)
Conference location Melbourne, Vic.
Conference dates 7 Nov. 2012
Title of proceedings ATIS 2012 : Proceedings of the 3rd Applications and Technologies in Information Security Workshop
Editor(s) Warren, Matthew
Publication date 2012
Conference series Applications and Technologies in Information Security Workshop
Start page 11
End page 16
Total pages 6
Publisher School of Information Systems, Deakin University
Place of publication Melbourne, Vic.
Keyword(s) phishing websites
ensemble classifiers
multi-tier ensembles
random forest
Summary This article is devoted to large multi-tier ensemble classifiers generated as ensembles of ensembles and applied to phishing websites. Our new ensemble construction is a special case of the general and productive multi-tier approach well known in information security. Many efficient multi-tier classifiers have been considered in the literature. Our new contribution is in generating new large systems as ensembles of ensembles by linking a top-tier ensemble to another middletier ensemble instead of a base classifier so that the top~ tier ensemble can generate the whole system. This automatic generation capability includes many large ensemble classifiers in two tiers simultaneously and automatically combines them into one hierarchical unified system so that one ensemble is an integral part of another one. This new construction makes it easy to set up and run such large systems. The present article concentrates on the investigation of performance of these new multi~tier ensembles 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 multi~level ensembles with three tiers. The results presented here demonstrate that new three-tier ensemble classifiers performed better than the base classifiers and standard ensembles included in the system. This example of application to the classification of phishing websites shows that the new method of combining diverse ensemble techniques into a unified hierarchical three-tier ensemble can be applied to increase the performance of classifiers in situations where data can be processed on a large computer.
ISBN 9780987229823
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2012, Deakin University
Persistent URL http://hdl.handle.net/10536/DRO/DU:30051355

Document type: Conference Paper
Collections: School of Information Technology
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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.