Supervised anomaly detection in uncertain pseudoperiodic data streams

Ma, Jiangang; Sun, Le; Wang, Hua; Zhang, Yanchun; Aickelin, Uwe

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.

Citation

Ma, J., Sun, L., Wang, H., Zhang, Y., & Aickelin, U. (2016). Supervised anomaly detection in uncertain pseudoperiodic data streams. ACM Transactions on Internet Technology, 16(1), https://doi.org/10.1145/2806890

• Journal Article Type: Article
• Acceptance Date: Mar 11, 2016
• Publication Date: Feb 24, 2016
• Deposit Date: Jun 15, 2016
• Publicly Available Date: Jun 15, 2016
• Journal: ACM Transactions on Internet Technology
• Print ISSN: 1533-5399
• Electronic ISSN: 1533-5399
• Publisher: Association for Computing Machinery (ACM)
• Peer Reviewed: Peer Reviewed
• Volume: 16
• Issue: 1
• DOI: https://doi.org/10.1145/2806890
• Public URL: https://nottingham-repository.worktribe.com/output/775478
• Publisher URL: http://dl.acm.org/citation.cfm?doid=2869768.2806890
• 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