Detecting anomalies from high-dimensional wireless network data streams: a case study
Article
Article Title | Detecting anomalies from high-dimensional wireless network data streams: a case study |
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ERA Journal ID | 36486 |
Article Category | Article |
Authors | Zhang, Ji (Author), Gao, Qigang (Author), Wang, Hai (Author) and Wang, Hua (Author) |
Journal Title | Soft Computing |
Journal Citation | 15 (6), pp. 1195-1215 |
Number of Pages | 21 |
Year | 2011 |
Publisher | Springer |
Place of Publication | Germany |
ISSN | 1432-7643 |
1433-7479 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s00500-010-0575-1 |
Web Address (URL) | https://link.springer.com/article/10.1007/s00500-010-0575-1 |
Abstract | In this paper, we study the problem of anomaly detection in wireless network streams. We have developed a new technique, called Stream Projected Outlier deTector (SPOT), to deal with the problem of anomaly detection from multi-dimensional or high-dimensional data streams. We conduct a detailed case study of SPOT in this paper by deploying it for anomaly detection from a real-life wireless network data stream. Since this wireless network data stream is unlabeled, a validating method is thus proposed to generate the ground-truth results in this case study for performance evaluation. Extensive experiments are conducted and the results demonstrate that SPOT is effective in detecting anomalies from wireless network data streams and outperforms existing anomaly detection methods. |
Keywords | outlier detection; high-dimensional data; subspaces; data streams |
ANZSRC Field of Research 2020 | 460609. Networking and communications |
400904. Electronic device and system performance evaluation, testing and simulation | |
400608. Wireless communication systems and technologies (incl. microwave and millimetrewave) | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Department of Mathematics and Computing |
Dalhousie University, Canada | |
Saint Mary's University, Canada |
https://research.usq.edu.au/item/9zzqx/detecting-anomalies-from-high-dimensional-wireless-network-data-streams-a-case-study
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