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dc.contributor.authorXie, Miao
dc.contributor.authorHan, Song
dc.contributor.authorTian, Biming
dc.contributor.editorYi Mu, Gregorio Martinez and Geyong Min
dc.date.accessioned2017-01-30T12:47:24Z
dc.date.available2017-01-30T12:47:24Z
dc.date.created2012-02-19T20:01:01Z
dc.date.issued2011
dc.identifier.citationXie, Miao and Han, Song and Tian, Biming. 2011. Highly efficient distance-based anomaly detection through univariate with PCA in wireless sensor networks, in 10th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), Nov 16 2011. Changsha, China: IEEE.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/25234
dc.identifier.doi10.1109/TrustCom.2011.73
dc.description.abstract

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.

dc.publisherIEEE
dc.subjectanomaly detection
dc.subjectfeature reduction
dc.subjectwireless sensor networks
dc.titleHighly efficient distance-based anomaly detection through univariate with PCA in wireless sensor networks
dc.typeConference Paper
dcterms.source.startPage564
dcterms.source.endPage571
dcterms.source.titleProceedings of the 10th IEEE international conference on trust, security and privacy in computing and communications (TrustCom 2011)
dcterms.source.seriesProceedings of the 10th IEEE international conference on trust, security and privacy in computing and communications (TrustCom 2011)
dcterms.source.isbn978-1-4577-2135-9
dcterms.source.conference10th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom 2011)
dcterms.source.conference-start-dateNov 16 2011
dcterms.source.conferencelocationChangsha, China
dcterms.source.placeChina
curtin.departmentDigital Ecosystems and Business Intelligence Institute (DEBII)
curtin.accessStatusFulltext not available


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