## 3 Research Design
### Appling Machine- and Deep- Learning to OTT Consumer Classification
#### 3.1.2 Deep Learning  
After the [PERSON] (AI) beat [PERSON] (human) in the 2016 Google DeepMind Go challenge match, deep learning captured the attention of the worldwide public. However, research on deep learning had already been actively underway in academia and practical application fields. Deep learning is an extension of the artificial neural network, and it learns and makes decisions by configuring the number of layers that make up its neural network. It took a while for computer hardware to catch up, but with recent graphical processing-unit developments, deep learning has been widely applied in various fields. Deep learning automatically selects features through its training process. It does not require much assistance from domain experts and learns complex patterns of constantly evolving data [43]. As such, many related studies on internet traffic analysis have been published [49]. MLP and CNN deep-learning methods are used for this paper. **The MLP** has a simple deep-learning structure and comprises an input layer, an output layer, and a hidden layer of neurons. Figure 2 shows the structure of the MLP. In each layer, several neurons are connected to the adjacent layer. The neurons calculate the weighted sum of inputs and output the results via a nonlinear activation function. In this process, the MLP uses a supervised back-propagation learning method. Because all nodes are connected within the MLP, each node in each layer has a specific weight, \(w_{ij}\), with all nodes of the adjacent layer. Node weights are adjusted based on back-propagation, which minimizes the error of the overall result [49]. However, the MLP method has the disadvantage of being very complex and inefficient, owing to the huge number of variables the model must learn [43]. Accordingly, to use an MLP, it is necessary to acquire data that is not too complex or to pay close attention to time consumption. The OTT dataset used in this study has quantitative values for each feature. Since it does not have complicated feature structures such as images or videos, it shows sufficiently good performance even if only simple MLP is applied. Therefore, we tried to save the time required for learning and detection by using the simplest MLP structure possible. In this study, we conduct an experiment with an input layer, an output layer, and a single hidden layer between them. **The CNN** is similar to the MLP, comprises several layers, and updates variables through learning. Although the MLP does not handle multi-dimensional inputs well, the CNN does so by applying a convolution layer. Figure 3 shows the structure of the CNN. The convolution layer produces results for the next layer by using kernels with learnable variables as inputs. The local filter is used to complete the mapping process, which is regarded as a convolution function. Additionally, because it is replicated in units, it shares the same weight vector and bias, thus increasing efficiency by greatly reducing the number of parameters. CNNs use a pooling process for down-sampling and can be widely applied to a variety of classifications. If the dimension of a vector used in the CNN process is 1, 2, or 3, it corresponds to 1D-, 2D-, and 3D-CNNs, respectively. The 1D-CNN is suitable for sequential data (e.g., language), the 2D-CNN is suitable for images or audio, and the 3D-CNN is suitable for video or large-volume images. Although there has been no research on classifying OTT traffic data by CNN, a study analyzing general network traffic via CNNs utilized a 1D-CNN [50]. This is because traffic characteristics are sequential; therefore, 1D-CNNs are sufficient and multi-dimensional CNNs are not required. In this study as well, OTT traffic was analyzed using a 1D-CNN since OTT traffic is similar to traditional traffic data. In addition, the filtering and pooling processes were performed using two convolutional layers as the complexity of the dataset was not high.