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Effective anomaly detection in sensor networks data streams

Saha, Budhaditya, Pham, Duc-Son, Lazarescu, Mihai and Venkatesh, Svetha 2009, Effective anomaly detection in sensor networks data streams, in ICDM 2009 : Proceedings of the 9th IEEE International Conference on Data Mining, IEEE, [Washington, D. C.], pp. 722-727.

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Title Effective anomaly detection in sensor networks data streams
Author(s) Saha, BudhadityaORCID iD for Saha, Budhaditya orcid.org/0000-0001-8011-6801
Pham, Duc-Son
Lazarescu, Mihai
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name International Conference on Data Mining (9th : 2009 : Miami, Fla.)
Conference location Miami, Fla.
Conference dates 6-9 Dec. 2009
Title of proceedings ICDM 2009 : Proceedings of the 9th IEEE International Conference on Data Mining
Editor(s) [Unknown]
Publication date 2009
Conference series International Conference on Data Mining
Start page 722
End page 727
Total pages 6
Publisher IEEE
Place of publication [Washington, D. C.]
Keyword(s) anomaly detection
compressed sensing
residual analysis
spectral methods
stream data processing
Summary This paper addresses a major challenge in data mining 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 largescale 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.
Notes 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.
ISBN 1424452422
9781424452422
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2009, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044568

Document type: Conference Paper
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.