## 2 Related Work  
Many studies have focused on network traffic classification technologies. Classical studies involve rule-based or statistical correlation-based network traffic classification. Machine learning has been researched extensively and studies on network traffic classification using machine learning have been actively conducted [18; 19; 21; 22; 23; 24; 25; 26; 27; 28; 29]. [PERSON] et al. [17] attempted to classify network traffic by machine learning using different kinds of datasets. They used the three ML algorithms: multi-layer perceptron, C4.5 decision tree, and support vector machine. As a result, the C4.5 decision tree algorithm showed better performance than the other two algorithms. [PERSON] et al. [18] used an unsupervised machine learning approach for network traffic classification. In this paper, the unsupervised K-means and the expectation maximization algorithm were used to cluster the network traffic application based on the similarity between them. SDN is an emerging networking paradigm that gives hope to change the limitations of current network infrastructures. SDN as a concept separates data plane and control plane to confront limitations and challenges of today's networking. In particular, since the SDN is logically centralized, controllers have a global visibility of the whole network unlike current networking. Furthermore, traffic scheduling and QoS control become easier and feasible for network administrators. [PERSON] et al. [19] considered traffic scheduling in SDN where several DPI proxy nodes are available for serving flows from ingress switches. Additionally, they studied a problem to minimize the delay of DPI processing, and designed a two-phase algorithm that can quickly select proxy and find routing paths for incoming flows. They also proposed a rule multiplexing scheme [20], in which a set of rules deployed on each node apply to the whole flow of a session going through yet toward different paths to deal with an efficient rule placement in QoS guaranteed multipath routing. Additionally, research on traffic classification using traditional machine learning is actively underway. [PERSON] et al. [21] introduced a network traffic classification that occurs in software-definednetworking (SDN). Four neural network estimators were used to classify traffic from SDN networks: Feedforward Neural Network, Multi-layer Perceptron (MLP), Levenberg-Marquardt (NARX), and Native Bayes (NARX). The four Neural Network Estimators provided 95.6%, 97%, 97%, and 97.6% in terms of accuracy, respectively. [PERSON] et al. [22] proposed a novel SDN flow classification framework using DPI and semi-supervised learning multiple classifiers. Three types of mechanisms known as Heteroid Tri-Training, Tri-Training, and Co-Training were used as semi-supervised multiple classifiers. Based on the result of semi-supervised multiple classifier, the proposed architecture classified network traffic flows into different QoS categories. [PERSON] et al. [23] deployed a simple architecture in an enterprise network that gathers traffic data using the OpenFlow protocol. After the traffic collected through the OpenFlow protocol was generated as training data, the network traffic was classified using Random Forests, Stochastic Gradient Boosting, and Extreme Gradient Boosting algorithms. However, these previous works do not utilize recent deep learning models. Most traditional machine learning requires a feature engineering process to reduce the complexity of data and find patterns in the dataset. The feature engineering requires experts to create a process for identifying data and extracting patterns themselves, and takes a long time. However, the deep learning models reduce the task of extracting new features from all input data. In general, the deep learning models learn the low-level features of input data in the initial layer and then the high-level representation of data in the layer.