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

    116349_Multiclassifier%20Classification%20of-04663326.pdf (321.3Kb)
    Access Status
    Open access
    Authors
    Islam, Md.
    Singh, Jaipal
    Chonka, A.
    Zhou, W.
    Date
    2008
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    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.

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