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Consensus clustering and supervised classification for profiling phishing emails in internet commerce security

Version 2 2024-06-04, 15:49
Version 1 2017-08-03, 12:12
conference contribution
posted on 2024-06-04, 15:49 authored by Richard DazeleyRichard Dazeley, John YearwoodJohn Yearwood, BH Kang, AV Kelarev
This article investigates internet commerce security applications of a novel combined method, which uses unsupervised consensus clustering algorithms in combination with supervised classification methods. First, a variety of independent clustering algorithms are applied to a randomized sample of data. Second, several consensus functions and sophisticated 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 supervised 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 Centre for Informatics and Applied Optimization. Our experiments compare the performance of several classification algorithms incorporated in this scheme. © 2010 Springer-Verlag Berlin Heidelberg.

History

Volume

6232 LNAI

Pagination

235-246

Location

Daegue, Korea

Start date

2010-08-20

End date

2010-09-03

ISSN

0302-9743

eISSN

1611-3349

ISBN-10

3642150365

Publication classification

EN.1 Other conference paper

Title of proceedings

PKAW: Pacific Rim Knowledge Acquisition Workshop

Publisher

Springer

Place of publication

Berlin, Germany

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