Time series network traffic analysis and forecasting are important for fundamental to many decision-making processes, also to understand network performance, reliability and security, as well as to identify potential problems. This paper provides the latest work on London South Bank University (LSBU) network data traffic analysis by adapting nonlinear autoregressive exogenous model (NARX) based on Levenberg-Marquardt backpropagation algorithm. This technique can analyse and predict data usage in its current and future states, as well as visualise the hourly, daily, weekly, monthly, and quarterly activities with less computation requirement. Results and analysis proved the accuracy of the prediction techniques.

Network monitoring and analysis are very important, in order to understand the performance of the networks, the reliability of the networks, the security of the networks, and to identify potential problems. In this chapter, we present our latest work on university network data traffic analysis by using continuous wavelet transform (CWT). With CWT, you can analyse the data and show how the frequency content of the data changes over time. This time dependent frequency varying information, which is lacking in other techniques, such FFT, is very useful for network traffic analysis. A twelve month's network traffic data, include World Wide Web (WWW) data and Email data were presented in 3D format, by using wavelet transform, we can visualise the hourly, daily, weekly and monthly activities. We will first present the theoretical background, then show the experimental results.