Machine learning approaches for modeling spammer behavior

Islam, Md. Saiful, Mahmud, Abdullah Al and Islam, Md Rafiqul 2010, Machine learning approaches for modeling spammer behavior. In Cheng, Pu-Jen, Kan, Min-Yen, Lam, Wai and Nakov, Preslav (ed), Information retrieval technology : 6th Asia Information Retrieval Symposium, AIRS 2010, Taipei, Taiwan, December 1-3, 2010 : proceedings, Springer-Verlag, Berlin, Germany, pp.251-260.

Attached Files
Name Description MIMEType Size Downloads

Title Machine learning approaches for modeling spammer behavior
Author(s) Islam, Md. Saiful
Mahmud, Abdullah Al
Islam, Md Rafiqul
Title of book Information retrieval technology : 6th Asia Information Retrieval Symposium, AIRS 2010, Taipei, Taiwan, December 1-3, 2010 : proceedings
Editor(s) Cheng, Pu-Jen
Kan, Min-Yen
Lam, Wai
Nakov, Preslav
Publication date 2010
Series Lecture Notes in Computer Science; v.6458
Chapter number 24
Total chapters 57
Start page 251
End page 260
Total pages 10
Publisher Springer-Verlag
Place of Publication Berlin, Germany
Keyword(s) spam email
Naive Bayes
Summary Spam is commonly known as unsolicited or unwanted email messages in the Internet causing potential threat to Internet Security. Users spend a valuable amount of time deleting spam emails. More importantly, ever increasing spam emails occupy server storage space and consume network bandwidth. Keyword-based spam email filtering strategies will eventually be less successful to model spammer behavior as the spammer constantly changes their tricks to circumvent these filters. The evasive tactics that the spammer uses are patterns and these patterns can be modeled to combat spam. This paper investigates the possibilities of modeling spammer behavioral patterns by well-known classification algorithms such as Naïve Bayesian classifier (Naive Bayes), Decision Tree Induction (DTI) and Support Vector Machines (SVMs). Preliminary experimental results demonstrate a promising detection rate of around 92%, which is considerably an enhancement of performance compared to similar spammer behavior modeling research.
ISBN 9783642171864
ISSN 0302-9743
Language eng
Field of Research 080303 Computer System Security
Socio Economic Objective 890206 Internet Hosting Services (incl. Application Hosting Services)
HERDC Research category B1 Book chapter
HERDC collection year 2010
Copyright notice ©2010, Springer
Persistent URL

Connect to link resolver
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 8 times in TR Web of Science
Scopus Citation Count Cited 12 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 326 Abstract Views, 6 File Downloads  -  Detailed Statistics
Created: Mon, 11 Apr 2011, 15:26:27 EST by Sandra Dunoon

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