1 Internet Traffic Prediction Using Recurrent Neural Networks Mircea Eugen Dodan 1 , Quoc-Tuan Vien 1,* and Tuan T. Nguyen 2 1 Faculty of Science and Technology, Middlesex University, United Kingdom. Email: mircea.eugen.dodan@gmail.com; q.vien@mdx.ac.uk. 2 School of Computing and Mathematical Sciences, University of Greenwich, United Kingdom. Email: Tuan.Nguyen@greenwich.ac.uk. Abstract Network traffic prediction (NTP) represents an essential component in planning large-scale networks which are in general unpredictable and must adapt to unforeseen circumstances. In small to medium-size networks, the administrator can anticipate the fluctuations in traffic without the need of using forecasting tools, but in the scenario of large-scale networks where hundreds of new users can be added in a matter of weeks, more efficient forecasting tools are required to avoid congestion and over provisioning. Network and hardware resources are however limited; and hence resource allocation is critical for the NTP with scalable solutions. To this end, in this paper, we propose an efficient NTP by optimizing recurrent neural networks (RNNs) to analyse the traffic patterns that occur inside flow time series, and predict future samples based on the history of the traffic that was used for training. The predicted traffic with the proposed RNNs is compared with the real values that are stored in the database in terms of mean squared error, mean absolute error and categorical cross entropy. Furthermore, the real traffic samples for NTP training are compared with those from other techniques such as auto-regressive moving average (ARIMA) and AdaBoost regressor to validate the effectiveness of the proposed method. It is shown that the proposed RNN achieves a better performance than both the ARIMA and AdaBoost regressor when more samples are employed. Keywords: Internet traffic prediction; recurrent neural networks; network planning Received on 09 June 2022, accepted on 28 August 2022, published on 02 September 2022 Copyright © 2022 Mircea Eugen Dodan et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited. doi: 10.4108/eetinis.v9i4.1415 * Corresponding author. Email: q.vien@mdx.ac.uk 1. Introduction Over the last couple of decades, network traffic has become increasingly more diverse and complex. According to [1], the global IP traffic reached 1.2 zeta Bytes (ZB) of data in 2016 and such trend is growing exponentially. Considering this exponential growth in traffic, it is useful for a network manager to have more efficient tools that would help to make more reliable decisions in planning future expansions of the network and consider a better management of resources including bandwidth allocation for certain flows while at the same time alleviating congestion in the network [2], [3]. The forecasting of traffic is also important in the security field, since unusual patterns in traffic can be detected and compared with the predicted results [4], [5], in case of botnets attacks. There are techniques such as [6] that do not require machine learning or artificial intelligence in order to forecast traffic, but they are not reliable since long-term dependencies are not considered. Network traffic prediction (NTP) represents a branch of network planning and capacity monitoring, that heavily depends on a set of historical data collected throughout the years. In order to make accurate forecasts about the future characteristics of the flows, multiple factors need to be considered in forecasting future traffic based on previous EAI Endorsed Transactions on Industrial Networks and Intelligent Systems Research Article EAI Endorsed Transactions on Industrial Networks and Intelligent Systems Online First