Supervised Anomaly Detection in Uncertain Pseudo-Periodic Data Streams

Medientyp: E-Book

Titel: Supervised Anomaly Detection in Uncertain Pseudo-Periodic Data Streams

Beteiligte: Ma, Jiangang [Verfasser:in]; Sun, Le [Sonstige Person, Familie und Körperschaft]; Wang, Hua [Sonstige Person, Familie und Körperschaft]; Zhang, Yanchun [Sonstige Person, Familie und Körperschaft]; Aickelin, Uwe [Sonstige Person, Familie und Körperschaft]

Erschienen: [S.l.]: SSRN, [2016]

Umfang: 1 Online-Ressource (21 p)

Sprache: Englisch

Entstehung:

Anmerkungen: In: ACM Transactions on Internet Technology (TOIT), Volume 16, Article No. 4, Issue 1, February 2016
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments March 11, 2016 erstellt

Beschreibung: 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

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