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Identification and Prediction of Internet Traffic Using Artificial Neural Networks

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This paper presents the development of an artificial neural network (ANN) model based on the multi-layer perceptron (MLP) for analyzing internet traffic data over IP networks. We applied the ANN to analyze a time series of measured data for network response evaluation. For this reason, we used the input and output data of an internet traffic over IP networks to identify the ANN model, and we studied the performance of some training algorithms used to estimate the weights of the neuron. The comparison between some training algorithms demonstrates the efficiency and the accu-racy of the Levenberg-Marquardt (LM) and the Resilient back propagation (Rp) algorithms in term of statistical crite-ria. Consequently, the obtained results show that the developed models, using the LM and the Rp algorithms, can successfully be used for analyzing internet traffic over IP networks, and can be applied as an excellent and fundamental tool for the management of the internet traffic at different times.

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The authors declare no conflicts of interest.

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S. Chabaa, A. Zeroual and J. Antari, "Identification and Prediction of Internet Traffic Using Artificial Neural Networks,"

*Journal of Intelligent Learning Systems and Applications*, Vol. 2 No. 3, 2010, pp. 147-155. doi: 10.4236/jilsa.2010.23018.

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