Fuzzy Vector Quantization for Network Intrusion Detection

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    Abstract

    This paper considers anomaly network traffic detection using different network feature subsets. Fuzzy c-means vector quantization is used to train network attack models and the minimum distortion rule is applied to detect network attacks. We also demonstrate the effectiveness and ineffectiveness in finding anomalies by looking at the network data alone. Experiments performed on the KDD CUP 1999 dataset show that time based traffic features in the last two second time window should be selected to obtain highest detection rates
    Original languageEnglish
    Title of host publicationProceedings: 2007 IEEE International Conference on Granular Computing (GrC 2007)
    EditorsT.Y Lin, X Hu
    Place of PublicationUnited States
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages566-570
    Number of pages5
    ISBN (Print)9780769530321
    DOIs
    Publication statusPublished - 2007
    EventIEEE International Conference on Granular Computing - San Jose, United States
    Duration: 2 Nov 20074 Nov 2007

    Conference

    ConferenceIEEE International Conference on Granular Computing
    CountryUnited States
    CitySan Jose
    Period2/11/074/11/07

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    Cite this

    Tran, D., Ma, W., Sharma, D., & Nguyen, T. (2007). Fuzzy Vector Quantization for Network Intrusion Detection. In T. Y. Lin, & X. Hu (Eds.), Proceedings: 2007 IEEE International Conference on Granular Computing (GrC 2007) (pp. 566-570). United States: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/GrC.2007.124