A deep learning model for accurate and robust internet traffic classification

Abstract

Network traffic classification is significant due to the fast growth of the number of internet users. The traditional way of classifying the large number of traffic generated by these users is becoming less effective. Therefore, many researchers made a network traffic classifier based on deep learning. However, those classifiers do not provide far better results and perform poorly when dealing with encrypted information. This paper tries to approach highly accurate and robust results in both encrypted and unencrypted networks by using machine learning algorithms. The algorithm used is the convolutional neural network (CNN). The performance of the proposed CNN is compared with that of the classical LeNet-5 network. Experimental results show that the classifier based on the proposed CNN performed better when dealing with both encrypted and unencrypted datasets, achieving a maximum average accuracy of 83.55%. Moreover, it is not sensitive to hyper-parameter choices, indicating its superiority in robustness. Compared with traditional network classifiers, the network classifier based on CNN can improve accuracy and improve stability.