Article

Unsupervised Outlier Detection in Sensor Networks Using Aggregation Tree

Authors:

Jianzhong LiAuthors Info & Claims

ADMA '07: Proceedings of the 3rd international conference on Advanced Data Mining and Applications

Pages 158 - 169

https://doi.org/10.1007/978-3-540-73871-8_16

Published: 06 August 2007 Publication History

Abstract

In the applications of sensor networks, outlier detection has attracted more and more attention. The identification of outliers can be used to filter false data, find faulty nodes and discover interesting events. A few papers have been published for this issue. However some of them consume too much communication, some of them need user to pre-set correct thresholds, some of them generate approximate results rather than exact ones. In this paper, a new unsupervised approach is proposed to detect global top n outliers in the network. This approach can be used to answer both snapshot queries and continuous queries. Two novel concepts, modifier set and candidate set for the global outliers, are defined in the paper. Also a commit-disseminate-verify mechanism for outlier detection in aggregation tree is provided. Using this mechanism and the these two concepts, the global top n outliers can be detected through exchanging short messages in the whole tree. Theoretically, we prove that the results generated by our approach are exact. The experimental results show that our approach is the most communication-efficient one compared with other existing methods. Moreover, our approach does not need any pre-specified threshold. It can be easily extended to multi-dimensional data, and is suitable for detecting outliers of various definitions.

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Cited By

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Chen YLi S(2019)A Lightweight Anomaly Detection Method Based on SVDD for Wireless Sensor NetworksWireless Personal Communications: An International Journal10.1007/s11277-019-06143-1105:4(1235-1256)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1007/s11277-019-06143-1
Saganowski ŁAndrysiak TKozik RChoraź M(2016)DWT-based anomaly detection method for cyber security of wireless sensor networksSecurity and Communication Networks10.1002/sec.15509:15(2911-2922)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1002/sec.1550
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cover image Guide Proceedings

ADMA '07: Proceedings of the 3rd international conference on Advanced Data Mining and Applications

August 2007

632 pages

ISBN:9783540738701

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 06 August 2007

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Cited By

Connor RSchuchard MHeninger NTraynor P(2019)Blind bernoulli trialsProceedings of the 28th USENIX Conference on Security Symposium10.5555/3361338.3361441(1483-1500)Online publication date: 14-Aug-2019
https://dl.acm.org/doi/10.5555/3361338.3361441
Chen YLi S(2019)A Lightweight Anomaly Detection Method Based on SVDD for Wireless Sensor NetworksWireless Personal Communications: An International Journal10.1007/s11277-019-06143-1105:4(1235-1256)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1007/s11277-019-06143-1
Saganowski ŁAndrysiak TKozik RChoraź M(2016)DWT-based anomaly detection method for cyber security of wireless sensor networksSecurity and Communication Networks10.1002/sec.15509:15(2911-2922)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1002/sec.1550
Titouna CAliouat MGueroui M(2015)Outlier Detection Approach Using Bayes Classifiers in Wireless Sensor NetworksWireless Personal Communications: An International Journal10.1007/s11277-015-2822-385:3(1009-1023)Online publication date: 1-Dec-2015
https://dl.acm.org/doi/10.1007/s11277-015-2822-3
Rahman MCarbunar BBallesteros JChau D(2015)To catch a fakeStatistical Analysis and Data Mining10.1002/sam.112648:3(147-161)Online publication date: 1-Jun-2015
https://dl.acm.org/doi/10.1002/sam.11264
Palee PSharp BNoriega LSebire NPlatt C(2013)Image analysis of histological features in molar pregnanciesExpert Systems with Applications: An International Journal10.1016/j.eswa.2013.06.03440:17(7151-7158)Online publication date: 1-Dec-2013
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https://dl.acm.org/doi/10.1504/IJBIDM.2012.051713
McDonald DMadria SErcal FBirmingham RLake TRolim JLuo JNikoletseas S(2012)CTODProceedings of the 10th ACM international symposium on Mobility management and wireless access10.1145/2386995.2386997(1-10)Online publication date: 24-Oct-2012
https://dl.acm.org/doi/10.1145/2386995.2386997
Chandola VBanerjee AKumar V(2009)Anomaly detectionACM Computing Surveys10.1145/1541880.154188241:3(1-58)Online publication date: 30-Jul-2009
https://dl.acm.org/doi/10.1145/1541880.1541882

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