On a statistical model of network traffic

A nonlinear analysis of traffic measurements involving a layered neural network (NN) (used for reconstruction of underlying process) has shown that the NN trained on a traffic series reproduces the packet size distribution of real data, which fits the lognormal form. The detailed analysis of the traffic measurements has shown that the reason for appearance of this distribution is a simple aggregation of real data. Applying the principal component analysis, the wavelet filtering, Fourier analysis, and statistical criteria we demonstrated that a few leading components form the main part of network traffic, while residual components play the role of small irregular variations that can be interpreted to be a stochastic noise.

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