A multi-tier ensemble construction of classifiers for phishing email detection and filtering

Abawajy, Jemal and Kelarev, Andrei 2012, A multi-tier ensemble construction of classifiers for phishing email detection and filtering, in Cyberspace safety and security : 4th International Symposium, CSS 2012 Melbourne, Australia, December 12-13, 2012 Proceedings, Springer-Verlag, Berlin, Germany, pp.48-56.

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Title A multi-tier ensemble construction of classifiers for phishing email detection and filtering
Author(s) Abawajy, Jemal
Kelarev, Andrei
Title of book Cyberspace safety and security : 4th International Symposium, CSS 2012 Melbourne, Australia, December 12-13, 2012 Proceedings
Editor(s) Xiang, Yang
Lopez, Javier
Kuo, C.C. Jay
Zhou, Wanlei
Publication date 2012
Series Lecture notes in computer science; v.7672
Chapter number 5
Total chapters 37
Start page 48
End page 56
Total pages 9
Publisher Springer-Verlag
Place of Publication Berlin, Germany
Summary This paper is devoted to multi-tier ensemble classifiers for the detection and filtering of phishing emails. We introduce a new construction of ensemble classifiers, based on the well known and productive multi-tier approach. Our experiments evaluate their performance for the detection and filtering of phishing emails. The multi-tier constructions are well known and have been used to design effective classifiers for email classification and other applications previously. We investigate new multi-tier ensemble classifiers, where diverse ensemble methods are combined in a unified system by incorporating different ensembles at a lower tier as an integral part of another ensemble at the top tier. Our novel contribution is to investigate the possibility and effectiveness of combining diverse ensemble methods into one large multi-tier ensemble for the example of detection and filtering of phishing emails. Our study handled a few essential ensemble methods and more recent approaches incorporated into a combined multi-tier ensemble classifier. The results show that new large multi-tier ensemble classifiers achieved better performance compared with the outcomes of the base classifiers and ensemble classifiers incorporated in the multi-tier system. This demonstrates that the new method of combining diverse ensembles into one unified multi-tier ensemble can be applied to increase the performance of classifiers if diverse ensembles are incorporated in the system.
ISBN 9783642353628
9783642353611
ISSN 0302-9743
0302-9743
Language eng
Field of Research 080501 Distributed and Grid Systems
Socio Economic Objective 890301 Electronic Information Storage and Retrieval Services
HERDC Research category B1 Book chapter
Copyright notice ©2012, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30051777

Document type: Book Chapter
Collection: School of Information Technology
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