Supervised Anomaly Detection in Uncertain Pseudoperiodic Data Streams
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 fra...
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| Published in | ACM transactions on Internet technology Vol. 16; no. 1; pp. 1 - 20 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
01.01.2016
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| Subjects | |
| Online Access | Get full text |
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| 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. |
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| AbstractList | 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. |
| Author | Aickelin, Uwe Ma, Jiangang Wang, Hua Zhang, Yanchun Sun, Le |
| Author_xml | – sequence: 1 givenname: Jiangang surname: Ma fullname: Ma, Jiangang organization: Victoria University, VIC, Australia – sequence: 2 givenname: Le surname: Sun fullname: Sun, Le organization: Victoria University, VIC, Australia – sequence: 3 givenname: Hua surname: Wang fullname: Wang, Hua organization: Victoria University, VIC, Australia – sequence: 4 givenname: Yanchun surname: Zhang fullname: Zhang, Yanchun organization: Victoria University, VIC, Australia – sequence: 5 givenname: Uwe surname: Aickelin fullname: Aickelin, Uwe organization: University of Nottingham, UK |
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| SubjectTerms | Anomalies Applications programs Classification Computational efficiency Data transmission Feature extraction Internet Pattern recognition |
| Title | Supervised Anomaly Detection in Uncertain Pseudoperiodic Data Streams |
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