## 4 Results and Discussion
### Time-Aware Consumer Classification
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \begin{tabular}{c} **Number of Data** \\ **First Filtered** \\ \end{tabular} & \begin{tabular}{c} **Number of Data** \\ **Incorrectly Filtered** \\ \end{tabular} & \
\begin{tabular}{c} **Number of Data Processed** \\ **by Deep Learning** \\ \end{tabular} &
\begin{tabular}{c} **Time Taken for** \\ **Detection (s)** \\ \end{tabular} \\ \cline{2-7}  & **KNN** & **J48** & **KNN** & **J48** & **KNN** & **J48** & **KNN** & **J48** \\ \hline
1 & 1167 & 1117 & 89 & 65 & 414 & 464 & 71.6 & 68.7 \\
5 & 924 & 1009 & 14 & 27 & 657 & 572 & 111.6 & 88.3 \\
10 & 792 & 965 & 5 & 13 & 789 & 616 & 145.2 & 99.5 \\
15 & 719 & 950 & 2 & 7 & 862 & 631 & 146.9 & 102.2 \\
20 & 668 & 949 & 2 & 6 & 913 & 632 & 173.9 & 102.0 \\
25 & 633 & 953 & 2 & 19 & 948 & 628 & 177.5 & 101.8 \\
30 & 613 & 958 & 2 & 21 & 968 & 623 & 178.7 & 101.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Filtering result according to cost change. results of Table 6, with an increase in the cost setting, the ambiguous data were forwarded for accurate deep learning, leading to higher accuracy. In terms of accuracy, the results showed only a slight difference when classifying the entire dataset using deep learning. The filter using KNN showed higher accuracy, because it filtered less data than did the filter using J48, resulting in more data being processed during the classification step. Therefore, as observed, the use of the filter with KNN utilized more classification time than that did that of J48. Overall, the filters using KNN and J48 showed classification accuracies of 97 and 96%, respectively, with no significant difference from the value obtained using only deep learning. For both filters, with the cost set higher, the accuracy increased, but the classification time also increased, as shown in Figure 6. Overall, while the accuracy of KNN was high, it utilized more time. When analyzing OTT users, if a considerable amount of data must be analyzed, the focus should be on reducing the time by setting the cost low. Furthermore, if the data to be analyzed are relatively small or if there is sufficient time for analysis, accuracy can be improved by setting the cost high. Thus, optimal time and detection rates can be set while adjusting the cost according to the given environment. The dataset used in this study contained the metadata of 1581 people. Therefore, regardless of analysis time, a significant time difference was not observed. As mentioned, ISPs or OTT providers will likely face hundreds of thousands or millions of users. If the data corresponding to the actual number of users are analyzed using the proposed method, time savings will be clearly observed. To this end, by applying SMOTE [52], an oversampling method, we created a dataset with 159,681 instances. SMOTE is a technique for creating new samples based on existing samples. Unlike other oversampling techniques that simply duplicate existing samples, SMOTE creates synthetic data based on existing data. Therefore, it is possible to create a dataset that has similar characteristics to an existing dataset but has a much larger number of samples. We used SMOTE to create data with a large number of samples, similar to the real environment, and then verified our proposed framework. This amount was approximately 100 times larger than the original dataset. Table 7 shows the time differences between the methods using simple deep learning and the proposed framework based on the oversampled dataset. Because the