Effective Anomaly detection in Sensor Network Data Streams
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. Moti...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
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IEEE Computer Society
2009
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/12001 |
| _version_ | 1848747957409546240 |
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| author | Pham, DucSon Saha , Budhaditya Lazarescu, Mihai Venkatesh, Svetha |
| author2 | Wei Wang |
| author_facet | Wei Wang Pham, DucSon Saha , Budhaditya Lazarescu, Mihai Venkatesh, Svetha |
| author_sort | Pham, DucSon |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T06:57:24Z |
| format | Conference Paper |
| id | curtin-20.500.11937-12001 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:57:24Z |
| publishDate | 2009 |
| publisher | IEEE Computer Society |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-120012022-12-09T05:23:41Z Effective Anomaly detection in Sensor Network Data Streams Pham, DucSon Saha , Budhaditya Lazarescu, Mihai Venkatesh, Svetha Wei Wang Hillol Kargupta Sanjay Ranka Philip S. Yu Xindong Wu anomaly detection compressed sensing stream data processing spectral - methods residual analysis 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. 2009 Conference Paper http://hdl.handle.net/20.500.11937/12001 10.1109/ICDM.2009.110 IEEE Computer Society fulltext |
| spellingShingle | anomaly detection compressed sensing stream data processing spectral - methods residual analysis Pham, DucSon Saha , Budhaditya Lazarescu, Mihai Venkatesh, Svetha Effective Anomaly detection in Sensor Network Data Streams |
| title | Effective Anomaly detection in Sensor Network Data Streams |
| title_full | Effective Anomaly detection in Sensor Network Data Streams |
| title_fullStr | Effective Anomaly detection in Sensor Network Data Streams |
| title_full_unstemmed | Effective Anomaly detection in Sensor Network Data Streams |
| title_short | Effective Anomaly detection in Sensor Network Data Streams |
| title_sort | effective anomaly detection in sensor network data streams |
| topic | anomaly detection compressed sensing stream data processing spectral - methods residual analysis |
| url | http://hdl.handle.net/20.500.11937/12001 |