Supervised anomaly detection in uncertain pseudoperiodic data streamsTools 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.
Official URL: http://dl.acm.org/citation.cfm?doid=2869768.2806890
AbstractUncertain 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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