Journal
ACM TRANSACTIONS ON INTERNET TECHNOLOGY
Volume 16, Issue 1, Pages -Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2806890
Keywords
Design; Algorithms; Performance; Anomaly detection; uncertain data stream; segmentation; classification
Funding
- National Natural Science Foundation of China (NSFC) [61332013]
- Australian Research Council (ARC) [DP140100841, DP130101327, LP100200682]
Ask authors/readers for more resources
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 article, we present a novel framework that supports anomaly detection in uncertain data streams. The proposed framework adopts the wavelet soft-thresholding method to remove the noises or errors in data streams. Based on the refined 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 several real datasets.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available