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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Bibliographic Details
Published inACM transactions on Internet technology Vol. 16; no. 1; pp. 1 - 20
Main Authors Ma, Jiangang, Sun, Le, Wang, Hua, Zhang, Yanchun, Aickelin, Uwe
Format Journal Article
LanguageEnglish
Published 01.01.2016
Subjects
Anomalies
Applications programs
Classification
Computational efficiency
Data transmission
Feature extraction
Internet
Pattern recognition
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Summary: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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ISSN:1533-5399
1557-6051
DOI:10.1145/2806890