# A Novel Proprietary Internet Video Traffic Dataset Generation Algorithm
## 4 Analysis of Proprietary Internet Video Traffic Datasets
### Imbalance Ratio  
To assess the distribution balance of samples across different label categories in the proprietary Internet video traffic datasets, suppose all classes \(x_{1},x_{2},\ldots,x_{n}\) in the dataset are arranged in ascending order of magnitude and divided into quartiles, with the quartiles being the lower quartile for values in the 25% position and the upper quartile for values in the 75% position. Equation (2) shows the generic quartile estimation formula that allows the first- and third-quartile values to be calculated. Equation (1) gives the dimensionless quartile coefficient of the dispersion (\(cvq\)) calculation method based on Equation (2), where \(p\) can be either the lower or upper quartile with values of 0.25 and 0.75, respectively, \(n\) is the number of all classes in the dataset, \(k\) is the location index of a single class, and \(\alpha\) is a coefficient determined by \(p\) and \(n\). Symbol \(\lfloor\cdot\rfloor\) indicates a round downward integer operation. Another dimensionless quantity evaluation metric is the coefficient of variation (\(cv\)), which can also be used to measure the degree of dispersion. It is the ratio of the standard deviation and the mean of the number of samples from all classes as shown in Equation (5). We use the following metrics to measure the imbalance rate in the proprietary Internet video traffic datasets. \[cvq=\frac{q(0.75)-q(0.25)}{q(0.75)+q(0.25)} \tag{1}\]  
\[q(p)=x_{(k)}+\alpha(x_{(k+1)}-x_{(k)}) \tag{2}\]  
\[\alpha=p(n+1)-\lfloor p(n+1)\rfloor \tag{3}\]  
\[k=\lfloor p(n+1)\rfloor \tag{4}\]  
\[cv=\frac{\sqrt{n\sum_{i=1}^{n}\left(x_{i}-\frac{\sum_{i=1}^{n}x_{i}}{n} \right)^{2}}}{\sum_{i=1}^{n}x_{i}} \tag{5}\]  
As shown in Table 2, Application category datasets and Protocol category datasets exhibit the highest dimensionless quartile coefficient and coefficient of variation among all datasets. This is attributed to the substantial differences in the number of samples across various labels, as illustrated in Figures 2 and 3. The nDPI Twitch video quality category proprietary datasets exhibit the lowest \(cvq\) and \(cv\) coefficients among all datasets due to bal \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **Top Represented** & \multicolumn{2}{c}{**Top Represented**} & \multicolumn{2}{c}{**Top Represented**} \\  & **Application** & **Categories** & **Protocol Categories** & **Sample Count** & **Requested Server** \\ \hline nDPI Facebook Video & Unspecified & 41,156 & Unknown & 41,156 & www.facebook.com \\ Quality Category & SocialNetwork & 1800 & QUIC & 1696 & scontent.x.fbcdn.net \\ Proprietary Dataset & & TLS & 104 & gateway.facebook.com \\ \hline \multirow{3}{*}{nDPI Twitch Video} & Web & 198,352 & TLS & 202,108 & www.twitch.tv \\  & Unspecified & 46,364 & Unknown & 46,364 & static.twitchech.net \\ Quality Category & Video & 2696 & & & video-weaver.frn05.hls.trwn.net \\ Proprietary Dataset & Cloud & 1052 & & & static-cdn.jrw.net \\  & Media & 8 & & ggl.twitch.tv \\ \hline nDPI YouTube Video & Unspecified & 109,856 & Unknown & 109,856 & www.youtube.com \\ Quality Category & Web & 41,272 & TLS & 41,180 & r1—sn-5 go?yne6.google.com \\ Proprietary Dataset & Media & 3308 & QUIC & 3400 & signaler-pa.youtube.com \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Top represented application, protocol categories, and requested server names with quantities in three video quality category proprietary datasets. anced sample volume distribution across categories. In contrast, the corresponding TVQC dataset has _cvq_ and _cv_ coefficients that are over 11 times and 5 times higher, respectively. For YouTube and Facebook video quality datasets, the _cvq_ coefficient is approximately 0.5, indicating a medium-high value. However, the _cv_ coefficient for Facebook is more than twice that of YouTube. Overall, the _cvq_ and _cv_ coefficients in CICFlowmeter video quality datasets are slightly higher than those in the nDPI datasets.