Zhang, Ji and Gao, Qigang and Wang, Hai and Wang, Hua (2011) Detecting anomalies from high-dimensional wireless network data streams: a case study. Soft Computing: A Fusion of Foundations, Methodologies and Applications, 15 (6). pp. 1195-1215. ISSN 1432-7643
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Identification Number or DOI: doi: 10.1007/s00500-010-0575-1
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.
|Item Type:||Article (Commonwealth Reporting Category C)|
|Additional Information:||Author version not held. Published version can not be displayed. From the issue entitled 'Special issue on Recent advances on machine learning and Cybernetics'.|
|Uncontrolled Keywords:||outlier detection; high-dimensional data; subspaces; data streams|
|Fields of Research (FOR2008):||08 Information and Computing Sciences > 0805 Distributed Computing > 080503 Networking and Communications|
10 Technology > 1006 Computer Hardware > 100605 Performance Evaluation; Testing and Simulation of Reliability
10 Technology > 1005 Communications Technologies > 100510 Wireless Communications
|Socio-Economic Objective (SEO2008):||E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences|
|Deposited On:||20 Oct 2010 10:23|
|Last Modified:||02 May 2012 12:21|
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