Packet-based Network Traffic Classification Using Deep Learning

TLDR: This study generates packet-based datasets through their own network traffic pre-processing, and trains five deep learning models using the convolutional neural network (CNN) and residual network (ResNet) to perform network traffic classification.

Abstract: Recently, the advent of many network applications has led to a tremendous amount of network traffic. A network operator must provide quality of service for each application on the network. To accomplish this goal, various studies have focused on accurately classifying application network traffic. Network management requires technology to classify network traffic without the intervention of the network operator. In this study, we generate packet-based datasets through our own network traffic pre-processing. We train five deep learning models using the convolutional neural network (CNN) and residual network (ResNet) to perform network traffic classification. Finally, we analyze the network traffic classification performance of packet-based datasets using the f1 score of the CNN and ResNet deep learning models, and demonstrate their effectiveness.