Supervised anomaly detection in uncertain pseudoperiodic data streams

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

Research output: Journal PublicationArticlepeer-review

85 Citations (Scopus)
60 Downloads (Pure)


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.
Original languageEnglish
JournalACM Transactions on Internet Technology
Issue number1
Publication statusPublished - 24 Feb 2016


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