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    An Immunology-inspired Multi-engine Anomaly Detection System with Hybrid Particle Swarm Optimisations

    Access Status
    Fulltext not available
    Authors
    Jiang, F.
    Ling, S.
    Chan, Kit Yan
    Chaczko, Z.
    Leung, F.
    Frater, M.
    Date
    2012
    Type
    Conference Paper
    
    Metadata
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    Citation
    Jiang, Frank and Ling, Sai Ho and Chan, Kit Yan and Chaczko, Zenon and Leung, Frank and Frater, Michael. 2012. An Immunology-inspired Multi-engine Anomaly Detection System with Hybrid Particle Swarm Optimisations, in IEEE International Conference on Fuzzy Systems, Jun 10-15 2012, pp. 1279-1286. Sydney, NSW: IEEE.
    Source Title
    Proceedings of the IEEE International Conference on Fuzzy Systems
    Source Conference
    IEEE International Conference on Fuzzy Systems
    DOI
    10.1109/FUZZ-IEEE.2012.6251241
    ISBN
    978-1-4673-1505-0
    URI
    http://hdl.handle.net/20.500.11937/4665
    Collection
    • Curtin Research Publications
    Abstract

    In this paper, multiple detection engines with multilayered intrusion detection mechanisms are proposed for enhancing computer security. The principle is to coordinate the results from each single-engine intrusion alert system, which seamlessly integrates with a multiple layered distributed service-oriented structure. An improved hidden Markov model (HMM) is created for the detection engine which is capable of the immunology based self/nonself discrimination. The classifications of normal and abnormal behaviours of system calls are further examined by an advanced fuzzy-based inference process tuned by HPSOWM. Considering a real benchmark dataset from the public domain, our experimental results show that the proposed scheme can greatly shorten the training time of HMM and significantly reduce the false positive rate. The proposed HPSOWM works especially well for the efficient classification of unknown behaviors and malicious attacks.

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