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. Attached Files Name Description MIMEType Size Downloads venkatesh-effectiveanomaly-2009.pdf Published version application/pdf 323.03KB 567 Title Effective anomaly detection in sensor networks data streams Author(s) Saha, Budhaditya orcid.org/0000-0001-8011-6801 Pham, Duc-Son Lazarescu, Mihai 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 Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30044568 Document type: Conference Paper Collections: School of Information Technology Open Access Collection Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. 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. Versions Version Filter Type Fri, 20 Apr 2012, 11:32:45 EST Thu, 26 Apr 2012, 15:27:22 EST Thu, 26 Apr 2012, 15:29:43 EST Tue, 01 May 2012, 15:42:39 EST Tue, 24 Jun 2014, 11:41:48 EST Tue, 29 Jul 2014, 14:39:23 EST Tue, 06 Sep 2016, 18:43:15 EST Sat, 21 Oct 2017, 02:03:40 EST Tue, 05 Dec 2017, 12:31:20 EST Filtered Full Citation counts:   Cited 15 times in TR Web of Science Cited 23 times in Scopus Search Google Scholar Access Statistics: 354 Abstract Views, 556 File Downloads  -  Detailed Statistics Created: Fri, 20 Apr 2012, 11:32:45 EST

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