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    Effective Anomaly detection in Sensor Network Data Streams

    133823_133823.pdf (321.7Kb)
    Access Status
    Open access
    Authors
    Pham, DucSon
    Saha , Budhaditya
    Lazarescu, Mihai
    Venkatesh, Svetha
    Date
    2009
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Pham, Duc and Saha , Budhaditya and Lazarescu, Mihai and Venkatesh, Svetha. 2009. Effective Anomaly detection in Sensor Network Data Streams, in Wang, W. and Kargupta, H. and Ranka, S. and Yu, P. S. and Wu, X. (ed), ICDM 2009, Dec 6 2009, pp. 722-727. Miami, Florida,USA: IEEE Computer Society.
    Source Title
    The Ninth IEEE International Conference on Data Mining
    Source Conference
    ICDM 2009
    DOI
    10.1109/ICDM.2009.110
    ISBN
    9780769538952
    Faculty
    School of Science and Computing
    Department of Computing
    Faculty of Science and Engineering
    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/20.500.11937/12001
    Collection
    • Curtin Research Publications
    Abstract

    This paper addresses a major challenge in datamining applications where the full information about the underlying processes, such as sensor networks or large online database, cannot be practically obtained due to physical limitations such as low bandwidth or memory, storage, or computing power. Motivated by the recent theory on direct information sampling called compressed sensing (CS), we propose a framework for detecting anomalies from these large scale data mining applications where the full information is not practically possible to obtain. Exploiting the fact that the intrinsic dimension of the data in these applications are typically small relative to the raw dimension and the fact that compressed sensing is capable of capturing most information with few measurements, our work show that spectral methods that used for volume anomaly detection can be directly applied to the CS data with guarantee on performance. Our theoretical contributions are supported by extensive experimental results on large datasets which show satisfactory performance.

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