Deakin home > Deakin University Library > Deakin Research Online > An innovative spam filtering model based on support vector machine
Openly accessible

An innovative spam filtering model based on support vector machine

Islam, Md. Rafiqul, Chowdhury, Morshed and Zhou, Wanlei 2005, An innovative spam filtering model based on support vector machine, in CIMCA 2005 jointly with IAWTIC 2005 : proceedings, IEEE Computer Society, Los Alamitos, Calif., pp. 348-353.

Document type: Conference Paper
Collections: School of Engineering and Information Technology
Open Access Collection
Attached Files (Some files may be inaccessible until you login with your Deakin Research Online credentials)
Name Description MIMEType Size Downloads
zhou-innovativespam-2005.pdf Published version application/pdf 278.15KB 153

Title An innovative spam filtering model based on support vector machine
Author(s) Islam, Md. Rafiqul
Chowdhury, Morshed
Zhou, Wanlei
Conference name International Conference on Computational Intelligence for Modelling, Control and Automation (2005 : Vienna, Austria)
Conference location Vienna, Austria
Conference dates 28-30 November 2005
Title of proceedings CIMCA 2005 jointly with IAWTIC 2005 : proceedings
Editor(s) Mohammadian, M.
Publication date 2005
Conference series Computational Intelligence for Modelling, Control and Automation Conference
Start page 348
End page 353
Publisher IEEE Computer Society
Place of publication Los Alamitos, Calif.
Keyword(s) machine learning
spam
SVM
kernel
Summary Spam is commonly defined as unsolicited email messages and the goal of spam categorization is to distinguish between spam and legitimate email messages. Many researchers have been trying to separate spam from legitimate emails using machine learning algorithms based on statistical learning methods. In this paper, an innovative and intelligent spam filtering model has been proposed based on support vector machine (SVM). This model combines both linear and nonlinear SVM techniques where linear SVM performs better for text based spam classification that share similar characteristics. The proposed model considers both text and image based email messages for classification by selecting an appropriate kernel function for information transformation.
ISBN 0769525040
9780769525044
Language eng
Field of Research 080110 Simulation and Modelling
HERDC Research category E1 Full written paper - refereed
HERDC collection year 2006
Copyright notice ©2005, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30006016
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in Deakin Research Online is owned by the author, with all rights reserved.

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.

Versions
Version Filter Type
Access Statistics: 424 Abstract Views, 153 File Downloads  -  Detailed Statistics
Created: Mon, 07 Jul 2008, 09:57:22 EST