Using GRU based deep neural network for intrusion detection in software-defined networks

This paper considers the possibility of using machine learning methods in solving the problem of intrusion detection in software-defined networks (SDN). The work is devoted to the research and development of a network attack classifier, which is a core of the intrusion detection systems. To evaluate the methods, an existing data set was used, which includes network traffic records with a several different network attack scenarios. A comparison of machine learning methods implementing neural networks on a selected data set is presented. Based on the results, it can be concluded that the task of intrusion detection in software-defined networks can be successfully solved using deep neural networks. © Published under licence by IOP Publishing Ltd.