Supervised anomaly detection in uncertain pseudoperiodic data streams

Ma, Jiangang and Sun, Le and Wang, Hua and Zhang, Yanchun and Aickelin, Uwe (2016) Supervised anomaly detection in uncertain pseudoperiodic data streams. ACM Transactions on Internet Technology, 16 (1). ISSN 1533-5399

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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 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.

Item Type: Article
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
Schools/Departments: University of Nottingham, Ningbo Campus > Faculty of Science and Engineering > Division of Computer Science
University of Nottingham UK Campus > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.1145/2806890
Depositing User: Aickelin, Professor Uwe
Date Deposited: 15 Jun 2016 12:48
Last Modified: 14 Sep 2016 13:24
URI: http://eprints.nottingham.ac.uk/id/eprint/34046

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