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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.

Attaining equitable education for all is one of the sustainable development goals. Not only the gap of education between urban and rural students but also the gap of access to information and communications technology also exists. Thus, an effort to resolve this gap is necessary. This study combines novel engineering, which is the fusion of engineering and literacy educational methods with maker education, to create a sustainable model of maker education. Through this process, an instructional model for a novel engineering-based maker education is proposed that would enable the utilization of the model in rural elementary school environment. To achieve this, this study analyzed the literature regarding maker education, novel engineering, and design thinking to develop, draft, and verify internal validity through an expert's review and usability evaluation. In addition, this study includes an example class for rural elementary students with application of the model to verify the model's external validity and confirm that the model significantly improved the maker mindset. Finally, this study proposes final instructional model by modifying and improving the draft based on the recommendations derived from the validity verification process. The proposed instructional model was developed as a suitable form for rural school students, which can use low-cost teaching tools to lower the entry barrier to information and communications technology education. The model process developed in this study has the advantage of raising convergence problem-solving abilities and the maker mindset, in terms of a making project that fuses literature and engineering, among students.