Highly efficient distance-based anomaly detection through univariate with PCA in wireless sensor networks
Unsupervised anomaly detection (UAD) techniques have received increasing attention in wireless sensor networks (WSNs). However, the high dimensional training data often make sensor nodes unable to sustain in computation, and result in quite expensive communication overhead. The feature reduction tec...
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE
2011
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/25234 |
| _version_ | 1848751652007313408 |
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| author | Xie, Miao Han, Song Tian, Biming |
| author2 | Yi Mu |
| author_facet | Yi Mu Xie, Miao Han, Song Tian, Biming |
| author_sort | Xie, Miao |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Unsupervised anomaly detection (UAD) techniques have received increasing attention in wireless sensor networks (WSNs). However, the high dimensional training data often make sensor nodes unable to sustain in computation, and result in quite expensive communication overhead. The feature reduction techniques make great sense through the reduction of the dimensionality when the features are strongly interrelated. Among these UAD techniques, distance-based anomaly detection (DB-AD) is a special one that allows to be described by a probability model. Based on this observation, DB-AD is explored deeply with a feature reduction technique, principal component analysis (PCA). Through examining the proportion of the variance explained by the first principal component (PC), a new feature reduction approach is proposed for DB- AD in WSNs, which enables to reduce the dimensionality to one in any situation. Specifically, the first PC is alone used for representing the original data as long as it retains most of the variance; otherwise, the information loss is geometrically reverted to neutralize the error. By obtaining a tradeoff between the detection error and performance overload, this approach is significant for resource-constrained WSNs, as the computational complexity and communication overhead will be reduced to a fraction of the original magnitude. Finally, this approach is evaluated with a real WSN dataset. |
| first_indexed | 2025-11-14T07:56:07Z |
| format | Conference Paper |
| id | curtin-20.500.11937-25234 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:56:07Z |
| publishDate | 2011 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-252342023-01-27T05:26:33Z Highly efficient distance-based anomaly detection through univariate with PCA in wireless sensor networks Xie, Miao Han, Song Tian, Biming Yi Mu Gregorio Martinez Geyong Min anomaly detection feature reduction wireless sensor networks Unsupervised anomaly detection (UAD) techniques have received increasing attention in wireless sensor networks (WSNs). However, the high dimensional training data often make sensor nodes unable to sustain in computation, and result in quite expensive communication overhead. The feature reduction techniques make great sense through the reduction of the dimensionality when the features are strongly interrelated. Among these UAD techniques, distance-based anomaly detection (DB-AD) is a special one that allows to be described by a probability model. Based on this observation, DB-AD is explored deeply with a feature reduction technique, principal component analysis (PCA). Through examining the proportion of the variance explained by the first principal component (PC), a new feature reduction approach is proposed for DB- AD in WSNs, which enables to reduce the dimensionality to one in any situation. Specifically, the first PC is alone used for representing the original data as long as it retains most of the variance; otherwise, the information loss is geometrically reverted to neutralize the error. By obtaining a tradeoff between the detection error and performance overload, this approach is significant for resource-constrained WSNs, as the computational complexity and communication overhead will be reduced to a fraction of the original magnitude. Finally, this approach is evaluated with a real WSN dataset. 2011 Conference Paper http://hdl.handle.net/20.500.11937/25234 10.1109/TrustCom.2011.73 IEEE restricted |
| spellingShingle | anomaly detection feature reduction wireless sensor networks Xie, Miao Han, Song Tian, Biming Highly efficient distance-based anomaly detection through univariate with PCA in wireless sensor networks |
| title | Highly efficient distance-based anomaly detection through univariate with PCA in wireless sensor networks |
| title_full | Highly efficient distance-based anomaly detection through univariate with PCA in wireless sensor networks |
| title_fullStr | Highly efficient distance-based anomaly detection through univariate with PCA in wireless sensor networks |
| title_full_unstemmed | Highly efficient distance-based anomaly detection through univariate with PCA in wireless sensor networks |
| title_short | Highly efficient distance-based anomaly detection through univariate with PCA in wireless sensor networks |
| title_sort | highly efficient distance-based anomaly detection through univariate with pca in wireless sensor networks |
| topic | anomaly detection feature reduction wireless sensor networks |
| url | http://hdl.handle.net/20.500.11937/25234 |