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An application of novel clustering technique for information security

Beliakov, Gleb, Yearwood, John and Kelarev, Andrei 2011, An application of novel clustering technique for information security, in ATIS 2011 : Workshop proceedingof ATIS 2011. Melbourne, November 9th, 2011. Second Applications and Techniques in Information Security Workshop, School of Information Systems, Deakin University, Melbourne, pp. 6-11.

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Title An application of novel clustering technique for information security
Author(s) Beliakov, Gleb
Yearwood, John
Kelarev, Andrei
Conference name Applications and Techniques in Information Security Workshop (2nd : 2011 : Melbourne, Vic.)
Conference location Melbourne, Vic.
Conference dates 9 Nov. 2011
Title of proceedings ATIS 2011 : Workshop proceedingof ATIS 2011. Melbourne, November 9th, 2011. Second Applications and Techniques in Information Security Workshop
Editor(s) Warren, Matthew
Publication date 2011
Conference series Applications and Techniques in Information Security Workshop
Start page 6
End page 11
Total pages 6
Publisher School of Information Systems, Deakin University
Place of publication Melbourne
Keyword(s) consensus functions
clustering
classification
phishing websites
Summary This article presents experimental results devoted to a new application of the novel clustering technique introduced by the authors recently. Our aim is to facilitate the application of robust and stable consensus functions in information security, where it is often necessary to process large data sets and monitor outcomes in real time, as it is required, for example, for intrusion detection. Here we concentrate on the particular case of application to profiling of phishing websites. First, we apply several independent clustering algorithms to a randomized sample of data to obtain independent initial clusterings. Silhouette index is used to determine the number of clusters. Second, we use a consensus function to combine these independent clusterings into one consensus clustering . Feature ranking is used to select a subset of features for the consensus function. Third, we train fast supervised classification algorithms on the resulting consensus clustering in order to enable them to process the whole large data set as well as new data. The precision and recall of classifiers at the final stage of this scheme are critical for effectiveness of the whole procedure. We investigated various combinations of three consensus functions, Cluster-Based Graph Formulation (CBGF), Hybrid Bipartite Graph Formulation (HBGF), and Instance-Based Graph Formulation (IBGF) and a variety of supervised classification algorithms. The best precision and recall have been obtained by the combination of the HBGF consensus function and the SMO classifier with the polynomial kernel.
ISBN 9780987229809
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
HERDC collection year 2011
Copyright notice ©2011, Deakin University
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044865

Document type: Conference Paper
Collection: School of Information Technology
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