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Multilayer hybrid strategy for phishing email zero-day filtering

Chowdhury, M. U., Abawajy, J. H., Kelarev, A. V. and Hochin, T. 2016, Multilayer hybrid strategy for phishing email zero-day filtering, Concurrency and computation: practice and experience, In Press, pp. 1-12, doi: 10.1002/cpe.3929.

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Title Multilayer hybrid strategy for phishing email zero-day filtering
Author(s) Chowdhury, M. U.
Abawajy, J. H.
Kelarev, A. V.
Hochin, T.
Journal name Concurrency and computation: practice and experience
Season In Press
Start page 1
End page 12
Total pages 12
Publisher Wiley
Place of publication Chichester, Eng.
Publication date 2016-07-22
ISSN 1532-0626
1532-0634
Keyword(s) attribute selection
ensembles
filtering
multilayer strategy
phishing emails
pruning
Summary The cyber security threats from phishing emails have been growing buoyed by the capacity of their distributors to fine-tune their trickery and defeat previously known filtering techniques. The detection of novel phishing emails that had not appeared previously, also known as zero-day phishing emails, remains a particular challenge. This paper proposes a multilayer hybrid strategy (MHS) for zero-day filtering of phishing emails that appear during a separate time span by using training data collected previously during another time span. This strategy creates a large ensemble of classifiers and then applies a novel method for pruning the ensemble. The majority of known pruning algorithms belong to the following three categories: ranking based, clustering based, and optimization-based pruning. This paper introduces and investigates a multilayer hybrid pruning. Its application in MHS combines all three approaches in one scheme: ranking, clustering, and optimization. Furthermore, we carry out thorough empirical study of the performance of the MHS for the filtering of phishing emails. Our empirical study compares the performance of MHS strategy with other machine learning classifiers. The results of our empirical study demonstrate that MHS achieved the best outcomes and multilayer hybrid pruning performed better than other pruning techniques.
Language eng
DOI 10.1002/cpe.3929
Field of Research 080109 Pattern Recognition and Data Mining
080503 Networking and Communications
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Wiley
Persistent URL http://hdl.handle.net/10536/DRO/DU:30089350

Document type: Journal Article
Collection: School of Information Technology
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