Encrypted Network Traffic Classification Using Deep Learning Method

Abstract

With growing use of internet and online applications, network traffic classification could be much more useful nowadays, because managing network services and quality assurance, two key points in network structure, could be done easily using this kind of classification. Different methods are used for this task, including port-based classification, machine learning and some other algorithms that each of them had its own advantages and disadvantages. For eliminating such disadvantages, deep learning methods are new ways for doing this task due to the power and excellent performance they showed. Furthermore, most of the work done in this field are using non-encrypted traffic or encrypted traffic in mobile networks, but as we know, privacy of data is very important these days. In this article, with the use of deep learning neural network, encrypted traffic of non-mobile data is being classified. For this purpose, we use the UNB ISCX VPN-non-VPN dataset that includes encrypted and unencrypted traffic of different applications. Then we design an algorithm based on DNN that could classify these traffics effectively. Performance of the model was evaluated and 0.86 accuracy and 0.78 fl-score showed that model works well compared to other algorithms used in this area.