Application-Based Online Traffic Classification with Deep Learning Models on SDN Networks


  • Lin-Huang Chang Department of Computer Science, National Taichung University of Education, Taichung, Taiwan
  • Tsung-Han Lee Department of Computer Science, National Taichung University of Education, Taichung, Taiwan
  • Hung-Chi Chu Department of Information and Communication Engineering, Chaoyang University of Technology, Taiwan
  • Cheng-Wei Su Department of Computer Science, National Taichung University of Education, Taichung, Taiwan



software defined network, network traffic classification, deep learning, tensorflow


The traffic classification based on the network applications is one important issue for network management. In this paper, we propose an application-based online and offline traffic classification, based on deep learning mechanisms, over software-defined network (SDN) testbed. The designed deep learning model, resigned in the SDN controller, consists of multilayer perceptron (MLP), convolutional neural network (CNN), and Stacked Auto-Encoder (SAE), in the SDN testbed. We employ an open network traffic dataset with seven most popular applications as the deep learning training and testing datasets. By using the TCPreplay tool, the dataset traffic samples are re-produced and analyzed in our SDN testbed to emulate the online traffic service. The performance analyses, in terms of accuracy, precision, recall, and F1 indicators, are conducted and compared with three deep learning models.


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How to Cite

L.-H. Chang, Tsung-Han Lee, Hung-Chi Chu, and Cheng-Wei Su, “Application-Based Online Traffic Classification with Deep Learning Models on SDN Networks”, Adv. technol. innov., vol. 5, no. 4, pp. 216–229, Sep. 2020.