Openly accessible

Spam filtering using ML algorithms

Islam, Md. Rafiqul and Chowdhury, Morshed U. 2005, Spam filtering using ML algorithms, in Proceedings of the IADIS international conference WWW/Internet 2005, IADIS Press, Lisbon, Portugal, pp. 419-426.

Attached Files
Name Description MIMEType Size Downloads
chowdhury-spamfiltering-2005.pdf Published version application/pdf 468.80KB 6

Title Spam filtering using ML algorithms
Author(s) Islam, Md. Rafiqul
Chowdhury, Morshed U.
Conference name IADIS international conference WWW/Internet (2005 : Lisbon, Portugal)
Conference location Lisbon, Portugal
Conference dates 19-22 October 2005
Title of proceedings Proceedings of the IADIS international conference WWW/Internet 2005
Editor(s) Isaias, P.
Nunes, M. B.
Rodrigues, L.
Barbosa, P.
Publication date 2005
Conference series International Association for Development of the Information Society Conference on the WWW/Internet
Start page 419
End page 426
Publisher IADIS Press
Place of publication Lisbon, Portugal
Keyword(s) spam
SVM
kernel functions
machine learning (ML)
VC dimension
Summary Spam is commonly defined as unsolicited email messages, and the goal of spam categorization is to distinguish between spam and legitimate email messages. Spam used to be considered a mere nuisance, but due to the abundant amounts of spam being sent today, it has progressed from being a nuisance to becoming a major problem. Spam filtering is able to control the problem in a variety of ways. Many researches in spam filtering has been centred on the more sophisticated classifier-related issues. Currently,  machine learning for spam classification is an important research issue at present. Support Vector Machines (SVMs) are a new learning method and achieve substantial improvements over the currently preferred methods, and behave robustly whilst tackling a variety of different learning tasks. Due to its high dimensional input, fewer irrelevant features and high accuracy, the  SVMs are more important to researchers for categorizing spam. This paper explores and identifies the use of different learning algorithms for classifying spam and legitimate messages from e-mail. A comparative analysis among the filtering techniques has also been presented in this paper.
Notes IADIS (International Association for Development of the Information Society)
ISBN 972892402X
9789728924027
Language eng
Field of Research 080199 Artificial Intelligence and Image Processing not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2005, IADIS
Persistent URL http://hdl.handle.net/10536/DRO/DU:30005745

Document type: Conference Paper
Collections: School of Information Technology
Open Access Collection
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO 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: 437 Abstract Views, 6 File Downloads  -  Detailed Statistics
Created: Mon, 07 Jul 2008, 09:53:38 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.