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Supervised anomaly detection in uncertain pseudoperiodic data streams

Ma, Jiangang; Sun, Le; Wang, Hua; Zhang, Yanchun; Aickelin, Uwe

Authors

Jiangang Ma

Le Sun

Hua Wang

Yanchun Zhang

Uwe Aickelin



Abstract

Uncertain data streams have been widely generated in many Web applications. The uncertainty in data streams makes anomaly detection from sensor data streams far more challenging. In this paper, we present a novel framework that supports anomaly detection in uncertain data streams. The proposed framework adopts an efficient uncertainty pre-processing procedure to identify and eliminate uncertainties in data streams. Based on the corrected data streams, we develop effective period pattern recognition and feature extraction techniques to improve the computational efficiency. We use classification methods for anomaly detection in the corrected data stream. We also empirically show that the proposed approach shows a high accuracy of anomaly detection on a number of real datasets.

Citation

Ma, J., Sun, L., Wang, H., Zhang, Y., & Aickelin, U. (2016). Supervised anomaly detection in uncertain pseudoperiodic data streams. ACM Transactions on Internet Technology, 16(1), https://doi.org/10.1145/2806890

Journal Article Type Article
Acceptance Date Mar 11, 2016
Publication Date Feb 24, 2016
Deposit Date Jun 15, 2016
Publicly Available Date Jun 15, 2016
Journal ACM Transactions on Internet Technology
Print ISSN 1533-5399
Electronic ISSN 1533-5399
Publisher Association for Computing Machinery (ACM)
Peer Reviewed Peer Reviewed
Volume 16
Issue 1
DOI https://doi.org/10.1145/2806890
Public URL http://eprints.nottingham.ac.uk/id/eprint/34046
Publisher URL http://dl.acm.org/citation.cfm?doid=2869768.2806890
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf
Additional Information © ACM, 2016. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Jiangang Ma, Le Sun, Hua Wang, Yanchun Zhang, and Uwe Aickelin. 2016. Supervised anomaly detection
in uncertain pseudoperiodic data streams. ACM Trans. Internet Technol. 16, 1, Article 4 (January 2016), 20
p. http://doi.acm.org/10.1145/2869768.2806890

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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