Network traffic classification [1; 2; 3; 4] for providing different QoS according to each application has been being actively researched. There are various methods for network traffic classification, such as rule-based, correlation-based, and payload-based methods. Rule-based methods are widely used for network traffic classification [5; 6; 7]. They classify packets entering the network according to predefined rules. The classification methods usually use the header information of the network packet, and they are performed on the basis of the source and destination IP addresses and port numbers of the packet headers. Although these methods classify network traffic by well-known application port numbers or specific rules based on packet header information, the traffic is not well classified for unknown applications because network operators do not know the port number or specific rules in advance. Moreover, it is inconvenient for a network operator to manually add a rule or port number to provide a new network service. Correlation-based network classification methods classify network traffic by selecting packets with high correlation between traffic packets considering the correlation between network traffic. They usually find statistical flow characteristics in network traffic flow, and incorporate them into machine learning techniques, e.g., logistic regression, decision tree, and support vector machine [8; 9; 10]. As such statistical characteristics, packet size, packet arrival rate, flow duration, and inherent business is usually used. It has been reported that the classification accuracy is relatively high. However, they require additional calculations for correlations in each flow, and additional consumption occurs when constructing datasets based on the correlation. Payload-based network classification methods classify network traffic by using the pure application layer payload information excluding the packet header information of the entire network traffic [11; 12; 13; 14]. Currently, it is no longer accurate to classify network traffic with packet header information containing the well-known port numbers or IP addresses [15], since many IoT devices and mobile devices use private or dynamic IP addresses and variable port numbers. Payload-based network classification methods overcome the IP address and port number dependency problem because they are not affected even if the header information is changed. They are usually deployed with deep packet inspection (DPI) techniques. Through DPI, it is possible to compare the contents of packet payload against a set of rules, which are usually written in a string format. However, the usage of such format rules imposes strong limitations such as limited expressiveness and inability to cope with various complex services [16]. The study in the field of machine learning have progressed actively, and machine learning has been adopted in various areas [2]. Thus, network traffic classification using machine learning has been researched extensively [17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29]. Additionally, network traffic classification methods using machine learning in software defined networks (SDNs) are actively researched [21; 22; 23; 24; 25; 26; 27; 28; 29]. In particular, deep learning models [30; 31; 32] have been recently studied for network traffic classification since their performance is known to be usually better than other machine learning algorithms [27; 28; 29]. In the existing network traffic classification methods based on deep learning models [27; 28], however, classification is usually performed by the header information as well as the payload of packets as the learning feature. The limitation of such an approach can arise in case the head information is collected into a limited dataset collected within a local network and the deep learning model training is performed with such a dataset.