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

• Main Authors: Lin-Huang Chang, Tsung-Han Lee, Hung-Chi Chu, Cheng-Wei Su
• Format: Article
• Language: English
• Published: Taiwan Association of Engineering and Technology Innovation 2020-07-01
• Series: Advances in Technology Innovation
• Subjects: software defined network; network traffic classification; deep learning, tensorflow
• Online Access: http://ojs.imeti.org/index.php/AITI/article/view/4286
• ISSN: 2415-0436; 2518-2994

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.