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Open AccessArticle

Payload-Based Traffic Classification Using Multi-Layer LSTM in Software Defined Networks

1
Interdisciplinary Program in Creative Engineering, Korea University of Technology and Education, Cheonan 31253, Korea
2
Department of Computer Science & Engineering, Korea University of Technology and Education, Cheonan 31253, Korea
3
Electronics and Telecommunications Research Institute, Daejeon 34129, Korea
*
Author to whom correspondence should be addressed.
This paper is an extended version of the conference paper presented in the 1st International Conference on Artificial Intelligence in Information and Communication (ICAIIC 2019) [40].
Appl. Sci. 2019, 9(12), 2550; https://doi.org/10.3390/app9122550
Received: 8 May 2019 / Revised: 11 June 2019 / Accepted: 19 June 2019 / Published: 21 June 2019
Recently, with the advent of various Internet of Things (IoT) applications, a massive amount of network traffic is being generated. A network operator must provide different quality of service, according to the service provided by each application. Toward this end, many studies have investigated how to classify various types of application network traffic accurately. Especially, since many applications use temporary or dynamic IP or Port numbers in the IoT environment, only payload-based network traffic classification technology is more suitable than the classification using the packet header information as well as payload. Furthermore, to automatically respond to various applications, it is necessary to classify traffic using deep learning without the network operator intervention. In this study, we propose a traffic classification scheme using a deep learning model in software defined networks. We generate flow-based payload datasets through our own network traffic pre-processing, and train two deep learning models: 1) the multi-layer long short-term memory (LSTM) model and 2) the combination of convolutional neural network and single-layer LSTM models, to perform network traffic classification. We also execute a model tuning procedure to find the optimal hyper-parameters of the two deep learning models. Lastly, we analyze the network traffic classification performance on the basis of the F1-score for the two deep learning models, and show the superiority of the multi-layer LSTM model for network packet classification. View Full-Text
Keywords: traffic classification; recurrent neural network; long short-term memory; convolutional neural network; software defined networks traffic classification; recurrent neural network; long short-term memory; convolutional neural network; software defined networks
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Lim, H.-K.; Kim, J.-B.; Kim, K.; Hong, Y.-G.; Han, Y.-H. Payload-Based Traffic Classification Using Multi-Layer LSTM in Software Defined Networks. Appl. Sci. 2019, 9, 2550.

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