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    Chaos Theory Based Detection against Network Mimicking DDoS Attacks

    133173_StreamGate.pdf (403.5Kb)
    Access Status
    Open access
    Authors
    Chonka, A.
    Singh, Jaipal
    Zhou, W.
    Date
    2009
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Chonka, Ashley and Singh, Jaipal and Zhou, Wanlei. 2009. Chaos Theory Based Detection against Network Mimicking DDoS Attacks. IEEE Communication Letters. 13 (9): pp. 717-719.
    Source Title
    IEEE Communication Letters
    ISSN
    1089-7798
    Faculty
    Curtin Business School
    The Digital Ecosystems and Business Intelligence Institute (DEBII)
    School
    Digital Ecosystems and Business Intelligence Institute (DEBII)
    Remarks

    Copyright © 2009 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/20.500.11937/5327
    Collection
    • Curtin Research Publications
    Abstract

    DDoS attack traffic is difficult to differentiate from legitimate network traffic during transit from the attacker, or zombies, to the victim. In this paper, we use the theory of network self-similarity to differentiate DDoS flooding attack traffic from legitimate self-similar traffic in the network. We observed that DDoS traffic causes a strange attractor to develop in the pattern of network traffic. From this observation, we developed a neural network detector trained by our DDoS prediction algorithm. Our preliminary experiments and analysis indicate that our proposed chaotic model can accurately and effectively detect DDoS attack traffic. Our approach has the potential to not only detect attack traffic during transit, but to also filter it.

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