## 5 Conclusions  
In this study, we proposed machine- and deep-learning methods for OTT user analysis to provide ISPs and OTT providers critical timely information about OTT usage data so that they can effectively monitor and execute pricing and mitigation plans. By classifying users according to OTT usage, we confirmed that the classification accuracy was high when using deep learning and conventional machine-learning methods. In particular, deep learning showed higher accuracy. This implies that the application of deep learning to OTT user classification was successful. With plain deep learning, the accuracy of OTT user classification is high, but the classification time takes longer. To shorten this time requirement, we proposed a time-aware MetaCost filtering framework. After first filtering the obvious data using a relatively light algorithm, deep learning was applied to only the most ambiguous data, significantly reducing classification time. However, the accuracy was about the same as with plain deep learning. This study has the following implications for network and OTT providers. This is the first study that demonstrated how deep learning can be employed to classify OTT user behaviors in a timely manner. ISPs are heavily burdened with applying and maintaining requisite network infrastructure and load balancing to support not only OTT services, but all other internet services, much of which is privately or government contracted. Thus, these investments seriously drive strategy. Hence, timely and extremely accurate usage analysis is needed. This study, therefore, has a wide range of applications in all of those domains. The proposed framework drastically reduces the time consumption of deep-learning methods with respect to ever-changing user behavior. In fact, when business providers analyze this information, they must consider hundreds of thousands of data items at once. The analysis of such a large amount of data using deep learning can be prohibitively time-consuming and requires heavy computer-resource investments. When applying the proposed method, the costs of time consumption can be drastically reduced. The proposed method can be used to perform classification according to situations by adjusting the cost factor. In the case where the number of data is relatively small, or there is sufficient time or available resources, accuracy can be improved by increasing the number of data analyzed through deep learning (i.e., cost is set to \"high\"). On the contrary, if many cases must be analyzed promptly, the cost can be set to \"low\". Thus, the more obvious data are filtered out. As such, flexible responses are possible by adjusting the cost factor, and the proposed framework can be, therefore, used by providers for real analysis purposes. In the future, we plan to focus more on the following points. First, when using deep learning, there is a need for a customized methodology suitable for the particular dataset. Because the OTT dataset used in this study comprised unsophisticated features, a simple MLP or CNN resulted in \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Cost**} & \multicolumn{3}{c}{**J48 Filter + Deep Learning**} & \multirow{2}{*}{
\begin{tabular}{c} **Deep Learning** \\ **(MLP)** \\ \end{tabular} } \\ \cline{2-2} \cline{4-5}  & **J48** & **Deep Learning** & **Total** & **(MLP)** \\ \hline
1 & 568.3 & 8307.0 & 8875.3 & \\
5 & 2071.2 & 8397.2 & 10,468.4 & \\
10 & 2073.6 & 8310.3 & 10,838.9 & \\
15 & 2102.7 & 8443.2 & 10,545.9 & 20369.6 \\
20 & 2093.1 & 8303.8 & 10,396.9 & \\
25 & 2125.5 & 8302.0 & 10,427.5 & \\
30 & 2091.7 & 8304.9 & 10,396.6 & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Changes in time taken for classification according to cost change (unit: Second). significant outcomes. However, if complex data were to be analyzed instead, more complex deep learning algorithms must be used. Furthermore, analysis needs to be performed based on various types and categories of OTT user data.