Supervised anomaly detection in uncertain pseudoperiodic data streams
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 frame...
| Main Authors: | , , , , |
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| Format: | Article |
| Published: |
Association for Computing Machinery
2016
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| Online Access: | https://eprints.nottingham.ac.uk/34046/ |
| _version_ | 1848794761813557248 |
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| author | Ma, Jiangang Sun, Le Wang, Hua Zhang, Yanchun Aickelin, Uwe |
| author_facet | Ma, Jiangang Sun, Le Wang, Hua Zhang, Yanchun Aickelin, Uwe |
| author_sort | Ma, Jiangang |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:21:20Z |
| format | Article |
| id | nottingham-34046 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:21:20Z |
| publishDate | 2016 |
| publisher | Association for Computing Machinery |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-340462021-03-19T08:51:50Z https://eprints.nottingham.ac.uk/34046/ Supervised anomaly detection in uncertain pseudoperiodic data streams Ma, Jiangang Sun, Le Wang, Hua Zhang, Yanchun Aickelin, Uwe 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. Association for Computing Machinery 2016-02-24 Article PeerReviewed 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 http://dl.acm.org/citation.cfm?doid=2869768.2806890 doi:10.1145/2806890 doi:10.1145/2806890 |
| spellingShingle | Ma, Jiangang Sun, Le Wang, Hua Zhang, Yanchun Aickelin, Uwe Supervised anomaly detection in uncertain pseudoperiodic data streams |
| title | Supervised anomaly detection in uncertain pseudoperiodic data streams |
| title_full | Supervised anomaly detection in uncertain pseudoperiodic data streams |
| title_fullStr | Supervised anomaly detection in uncertain pseudoperiodic data streams |
| title_full_unstemmed | Supervised anomaly detection in uncertain pseudoperiodic data streams |
| title_short | Supervised anomaly detection in uncertain pseudoperiodic data streams |
| title_sort | supervised anomaly detection in uncertain pseudoperiodic data streams |
| url | https://eprints.nottingham.ac.uk/34046/ https://eprints.nottingham.ac.uk/34046/ https://eprints.nottingham.ac.uk/34046/ |