Multi-classifier classification of spam email on a ubiquitous multi-core architecture

Islam, Md. and Singh, Jaipal and Chonka, Ashley and Zhou, Wanlei. 2008. Multi-classifier classification of spam email on a ubiquitous multi-core architecture, in Cao, J. and Li, M. and Weng, Ch. and Xiang, Y. and Wang, X. and Tang, H. and Hong, F. and Liu, H. and Wang, Y. (ed), IFIP International Conference on Network and Parallel Computing (NPC) Workshops, Oct 18 2008, pp. 210-217. Shanghai, China: Institute of Electrical and Electronics Engineers (IEEE) Computer Society

Source Title

Proceedings of the IFIP international conference on network and parallel computing (NPC) workshops

Source Conference

IFIP International Conference on Network and Parallel Computing (NPC) Workshops

DOI

10.1109/NPC.2008.71

ISBN

9780769533544

Faculty

Curtin Business School

School of Information Systems

School

Centre for Extended Enterprises and Business Intelligence

Remarks

Copyright © 2008 IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

URI

http://hdl.handle.net/20.500.11937/28402

Collection

Curtin Research Publications

Abstract

This paper presents an innovative fusion based multi-classifier email classification on a ubiquitous multi-core architecture. Many approaches use text-based single classifiers or multiple weakly trained classifiers to identify spam messages from a large email corpus. We build upon our previous work on multi-core by apply our ubiquitous multi-core framework to run our fusion based multi-classifier architecture. By running each classifier process in parallel within their dedicated core, we greatly improve the performance of our proposed multi-classifier based filtering system. 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 the average cost of 1.4ms. We also reduced the instance of false positive, which is one of the key challenges in spam filtering system, and increases email classification accuracy substantially compared with single classification techniques.