Adaptive and Online One-Class Support Vector Machine-based Outlier Detection Techniques for Wireless Sensor Networks

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    Abstract

    Outlier detection in wireless sensor networks is essential to ensure data quality, secure monitoring and reliable detection of interesting and critical events. A key challenge for outlier detection in wireless sensor networks is to adaptively identify outliers in an online manner with a high accuracy while maintaining the resource consumption of the network to a minimum. In this paper, we propose one-class support vector machine-based outlier detection techniques that sequentially update the model representing normal behavior of the sensed data and take advantage of spatial and temporal correlations that exist between sensor data to cooperatively identify outliers. Experiments with both synthetic and real data show that our online outlier detection techniques achieve high detection accuracy and low false alarm rate.
    Original languageUndefined
    Title of host publicationProceedings of the IEEE 23rd International Conference on Advanced Information Networking and Applications Workshops/Symposia
    Place of PublicationBradford, United Kingdom
    PublisherIEEE Computer Society Press
    Pages990-995
    Number of pages6
    ISBN (Print)978-0-7695-3639-2
    DOIs
    Publication statusPublished - 26 May 2009
    Event23rd IEEE International Conference on Advanced Information Networking and Applications, AINA 2009 - Bradford, United Kingdom
    Duration: 26 May 200929 May 2009
    Conference number: 23
    http://www.inf.brad.ac.uk/~iawan/aina/

    Publication series

    Name
    PublisherIEEE Computer Society Press

    Workshop

    Workshop23rd IEEE International Conference on Advanced Information Networking and Applications, AINA 2009
    Abbreviated titleAINA
    CountryUnited Kingdom
    CityBradford
    Period26/05/0929/05/09
    Internet address

    Keywords

    • METIS-263863
    • IR-65500
    • EWI-15391

    Cite this

    Zhang, Y., Meratnia, N., & Havinga, P. J. M. (2009). Adaptive and Online One-Class Support Vector Machine-based Outlier Detection Techniques for Wireless Sensor Networks. In Proceedings of the IEEE 23rd International Conference on Advanced Information Networking and Applications Workshops/Symposia (pp. 990-995). Bradford, United Kingdom: IEEE Computer Society Press. https://doi.org/10.1109/WAINA.2009.200