Net Traffic Classification Based on GRU Network Using Sequential Features

Net traffic classification is the basis for providing various network services such as network security, network monitoring and Quality of Service (QoS) etc. Therefore, this field has always been a hot spot in academic and industrial research. Through proper data processing, the researcher said that it is possible to classify network flows through machine learning. Therefore, it is necessary to explore suitable processing methods and model structures. To our best knowledge, classification methods based on sequential feature learning are rarely discussed, so this paper proposes a model based on sequential features of net traffic. Different from the previous classification methods based on machine learning, the classification method based on GRU network focuses on exploring the sequential feature information of network traffic. This classification model based on deep learning is very suitable for big data processing. In terms of evaluation, we used the USTC-TFC2016 data set, compared with the basic model and previous methods, the experimental results show that: (1) the effectiveness of the sequential model for net traffic classification. (2) The sequential model has good performance in both accuracy and stability.