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Consensus clustering and supervised classification for profiling phishing emails in internet commerce security
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conference contribution
posted on 2024-06-04, 15:49 authored by Richard DazeleyRichard Dazeley, John YearwoodJohn Yearwood, BH Kang, AV KelarevThis 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 LNAIPagination
235-246Location
Daegue, KoreaPublisher DOI
Start date
2010-08-20End date
2010-09-03ISSN
0302-9743eISSN
1611-3349ISBN-10
3642150365Publication classification
EN.1 Other conference paperTitle of proceedings
PKAW: Pacific Rim Knowledge Acquisition WorkshopPublisher
SpringerPlace of publication
Berlin, GermanyUsage metrics
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