# A Novel Proprietary Internet Video Traffic Dataset Generation Algorithm
## 4 Analysis of Proprietary Internet Video Traffic Datasets  
For the data preprocessing described in the previous section, we used the nDACPC algorithm to generate six application and protocol category datasets: nILAD, nTLAD, nPAD, nLLAD, nILPD, nTLPD, nLPLD, and nPPD. Additionally, we leveraged the nYFTQC and CICQC algorithms to generate a total of fifteen proprietary video quality datasets: YVAIL, YVATL, YVALL, YVAP, FVAIL, FVATL, FVALL, FVAD, TVAIL, TVALL, TVAP, TVQC, YVQC, and FVQC. This section analyzes the generated dataset characteristics from three perspectives: the imbalance ratio, flow sample histogram density and cumulative distribution, and flow-level feature correlations. Figure 3 shows the flow record counts and distributions for YouTube, Facebook, and Twitch video quality category proprietary datasets. Across all datasets, the total number of flow records in the VLS mode consistently exceeds that in the VoD mode under identical resolution and frame rate conditions. Contrary to expectations, higher resolution does not necessarily result in more traffic or flow samples. Video traffic consumption is influenced by various video-related factors, and the number of flow samples reflects these complexities. For the nDPI video quality category datasets, all classes exhibit similar flow record counts within each category. However, Twitch generates more flow records in total and has higher sample volumes per category compared to YouTube and Facebook. In contrast, the CICFlowmeter video quality category datasets show that YouTube produces the most flow records overall. There is significant variation in sample size between categories; for instance, the 720P_30 FPS_live category has 13 times more flow samples than the 720P_30 FPS_VoD category. Additionally, CICFlowmeter datasets generally have category sample sizes five times larger than the corresponding nDPI categories in VLS mode. In VoD mode, the CICFlowmeter tool generates more flow samples for YouTube and Facebook compared to NFStream. However, on the Twitch platform, its sample volume is less than three times that of the corresponding NFStream dataset. Finally, the sample counts and distributions remain consistent across all accounting modes in the video quality proprietary datasets. It is worth noting that the proportions of applications and categories vary significantly after sequencing the selected video-attribute datasets. Table 1 presents the top application and protocol categories by flow sample count across different web video platforms. For the nDPI Facebook and YouTube video quality datasets, unknown flows constitute the majority, accounting for over 95% and 71% of total samples, respectively. TLS and QUIC are the two largest categories of parsed protocol categories. For the Twitch platform, TLS accounts for over 80% of the total flow volume, reflecting the trend of popular streaming platforms adopting HTTP/2 and HTTP/3 standards [30; 31]. These standards integrate advanced encryption protocols, making detection and classification increasingly challenging. The flow samples of application categories exhibit varying distribution patterns. In the Facebook and YouTube datasets, the unspecified category represents the highest proportion. However, Facebook traffic is limited to the SocialNetwork category, while YouTube traffic includes Web and Media categories, highlighting the nDPI library's limitations in parsing Facebook platform traffic. For Twitch, the Web category dominates flow volume, followed by Unspecified, Video, and Cloud applications. Table 1 also lists frequently decoded server names based on SNI, including facebook.com, twitch.tv, and youtube.com. While the total number of decoded server names is relatively small, they exhibit distinct characteristics. These request servers can be categorized into direct video streaming URLs, CDN accelerator cache  
Figure 3: Video quality category proprietary datasets sample distribution.