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Consensus Clustering and Supervised Classification for Profiling Phishing Emails in Internet Commerce Security


Dazeley, R and Yearwood, JL and Kang, BH and Kelarev, AHJ, Consensus Clustering and Supervised Classification for Profiling Phishing Emails in Internet Commerce Security, Knowledge Management and Acquisition for Smart Systems and Services, 20 Aug - 3 Sept 2010, Daegu, Korea, pp. 235-246. ISBN 978-3-642-15036-4 (2010) [Refereed Conference Paper]

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Copyright Springer-Verlag Berlin Heidelberg 2010


This article investigates internet commerce security applica- tions of a novel combined method, which uses unsupervised consensus clustering algorithms in combination with supervised classification meth- ods. First, a variety of independent clustering algorithms are applied to a randomized sample of data. Second, several consensus functions and so- phisticated algorithms are used to combine these independent clusterings into one final consensus clustering. Third, the consensus clustering of the randomized sample is used as a training set to train several fast super- vised classification algorithms. Finally, these fast classification algorithms are used to classify the whole large data set. One of the advantages of this approach is in its ability to facilitate the inclusion of contributions from domain experts in order to adjust the training set created by consensus clustering. We apply this approach to profiling phishing emails selected from a very large data set supplied by the industry partners of the Cen- tre for Informatics and Applied Optimization. Our experiments compare the performance of several classification algorithms incorporated in this scheme.

Item Details

Item Type:Refereed Conference Paper
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Knowledge representation and reasoning
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Kang, BH (Professor Byeong Kang)
ID Code:64975
Year Published:2010
Deposited By:Information and Communication Technology
Deposited On:2010-09-20
Last Modified:2014-12-09
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