Profiling phishing email based on clustering approach

Hamid, Isredza Rahmi A. and Abawajy, Jemal H. 2013, Profiling phishing email based on clustering approach, in TrustCom 2013 : Proceedings of the 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, IEEE Computer Society, Piscataway, N.J., pp. 628-635.

Attached Files
Name Description MIMEType Size Downloads

Title Profiling phishing email based on clustering approach
Author(s) Hamid, Isredza Rahmi A.
Abawajy, Jemal H.
Conference name IEEE Trust, Security and Privacy in Computing and Communications. Conference (12th : 2013 : Melbourne, Vic)
Conference location Melbourne, Vic
Conference dates 16-18 Jul. 2013
Title of proceedings TrustCom 2013 : Proceedings of the 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications
Editor(s) [Unknown]
Publication date 2013
Conference series IEEE International Conference on Trust, Security and Privacy in Computing and Communications
Start page 628
End page 635
Total pages 8
Publisher IEEE Computer Society
Place of publication Piscataway, N.J.
Keyword(s) profiling
phishing
clustering algorithm
Summary In this paper, an approach for profiling email-born phishing activities is proposed. Profiling phishing activities are useful in determining the activity of an individual or a particular group of phishers. By generating profiles, phishing activities can be well understood and observed. Typically, work in the area of phishing is intended at detection of phishing emails, whereas we concentrate on profiling the phishing email. We formulate the profiling problem as a clustering problem using the various features in the phishing emails as feature vectors. Further, we generate profiles based on clustering predictions. These predictions are further utilized to generate complete profiles of these emails. The performance of the clustering algorithms at the earlier stage is crucial for the effectiveness of this model. We carried out an experimental evaluation to determine the performance of many classification algorithms by incorporating clustering approach in our model. Our proposed profiling email-born phishing algorithm (ProEP) demonstrates promising results with the RatioSize rules for selecting the optimal number of clusters.
ISBN 9780769550220
Language eng
Field of Research 080303 Computer System Security
080110 Simulation and Modelling
080109 Pattern Recognition and Data Mining
Socio Economic Objective 890301 Electronic Information Storage and Retrieval Services
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
HERDC collection year 2013
Copyright notice ©2013, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30060748

Document type: Conference Paper
Collection: School of Information Technology
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Access Statistics: 18 Abstract Views, 3 File Downloads  -  Detailed Statistics
Created: Thu, 20 Feb 2014, 11:50:32 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.