Spam filtering for network traffic security on a multi-core environment

Islam, Rafiqul, Zhou, Wanlei, Xiang, Yang and Mahmood, Abdun Naser 2009, Spam filtering for network traffic security on a multi-core environment, Concurrency and computation : practice & experience, vol. 21, no. 10, pp. 1307-1320.

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Title Spam filtering for network traffic security on a multi-core environment
Author(s) Islam, Rafiqul
Zhou, Wanlei
Xiang, Yang
Mahmood, Abdun Naser
Journal name Concurrency and computation : practice & experience
Volume number 21
Issue number 10
Start page 1307
End page 1320
Total pages 14
Publisher John Wiley & Sons
Place of publication Bognor Regis, England
Publication date 2009
ISSN 1532-0626
1532-0634
Keyword(s) ubiquitous multi-core framework
multi-core
text classifier
multiple classifiers
multiple classifiers
classifier
spam
Summary This paper presents an innovative fusion-based multi-classifier e-mail classification on a ubiquitous multicore architecture. Many previous approaches used text-based single classifiers to identify spam messages from a large e-mail corpus with some amount of false positive tradeoffs. Researchers are trying to prevent false positive in their filtering methods, but so far none of the current research has claimed zero false positive results. In e-mail classification false positive can potentially cause serious problems for the user. In this paper, we use fusion-based multi-classifier classification technique in a multi-core framework. By running each classifier process in parallel within their dedicated core, we greatly improve the performance of our multi-classifier-based filtering system in terms of running time, false positive rate, and filtering accuracy. Our proposed architecture also provides a safeguard of user mailbox from different malicious attacks. Our experimental results show that we achieved an average of 30% speedup at an average cost of 1.4 ms. We also reduced the instances of false positives, which are one of the key challenges in a spam filtering system, and increases e-mail classification accuracy substantially compared with single classification techniques.
Language eng
Field of Research 080503 Networking and Communications
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
HERDC collection year 2009
Copyright notice ©2009, John Wiley & Sons
Persistent URL http://hdl.handle.net/10536/DRO/DU:30028925

Document type: Journal Article
Collection: School of Information Technology
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