Supervised anomaly detection in uncertain pseudoperiodic data streams

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

Full text not available from this repository.


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.
Schools/Departments: University of Nottingham Ningbo China > Faculty of Science and Engineering > School of Computer Science
University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number:
Depositing User: Aickelin, Professor Uwe
Date Deposited: 15 Jun 2016 12:48
Last Modified: 19 Mar 2021 08:51

Actions (Archive Staff Only)

Edit View Edit View