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Apply nonlinear dimensionality reduction method to large-scale communication network traffic analysis

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3 Author(s)
Weisong He ; Sch. of Commun. & Inf. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu ; Zhiping Li ; Hongmei Xiang

In this paper, we apply locally linear embedding method to large-scale communication network traffic analysis at flow-level. With the method, we can separate normal behavior from abnormal behavior by search k nearest neighbors. Compare with PCA, the method provide a nonlinear dimensionality reduction method for network traffic analysis and display more nonlinear information about data.

Published in:

Communications, Circuits and Systems, 2008. ICCCAS 2008. International Conference on

Date of Conference:

25-27 May 2008