Abstract
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.
Highlights
Much attention has been paid to the topic of complex networks, which characterize many natural and artificial systems such as internet, airline transport systems, power grid infrastructures, and the World Wide Web [1,2,3]
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
The criteria exploited in this study were the Root Mean Square Error (RMSE), the Scatter Index (SI), the Relative Error and Mean Absolute Percentage Error (MAPE) [64,65,66] given by:
Summary
Much attention has been paid to the topic of complex networks, which characterize many natural and artificial systems such as internet, airline transport systems, power grid infrastructures, and the World Wide Web [1,2,3]. Traffic modeling is fundamental to the network performance evaluation and the design of network control scheme which is crucial for the success of high-speed networks [4]. This is because network traffic capacity will help each webmaster to optimize their website, maximize online marketing conversions and lead campaign tracking [5,6]. Monitoring the efficiency and performance of IP networks based on accurate and advanced traffic measurements is an important topic in which research needs to explore a new scheme for monitoring network traffic and find out its proper approach [7]. The need for accurate traffic parameter prediction has long been recognized in the international scientific literature [8]
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