- Title
- Supervised anomaly detection in uncertain pseudoperiodic data streams
- Creator
- Ma, Jiangang; Sun, Le; Wang, Hua; Zhang, Yanchun; Aickelin, Uwe
- Date
- 2016
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/185617
- Identifier
- vital:16709
- Identifier
-
https://doi.org/10.1145/2806890
- Identifier
- ISBN:1533-5399
- 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 article, we present a novel framework that supports anomaly detection in uncertain data streams. The proposed framework adopts the wavelet soft-thresholding method to remove the noises or errors in data streams. Based on the refined 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 several real datasets.
- Publisher
- ACM
- Relation
- ACM transactions on Internet technology Vol. 16, no. 1 (2016), p. 1-20
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright ACM
- Subject
- Anomalies; Anomaly detection; Applications programs; Classification; Computational efficiency; Data transmission; Feature extraction; Internet; Pattern recognition; Segmentation; Uncertain data stream; 46 Information and computing sciences
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