# A Novel Proprietary Internet Video Traffic Dataset Generation Algorithm
## 2 Related Works
leveraged byte and time sequence features to classify traffic from 20 Google and Apple applications and 58 web services, achieving over 98% accuracy using the TLS Flow Sequence Network (TFSN) model on a dataset of 13,915 TLS flow samples [13]. [PERSON] et al. proposed the Extended Byte Segment Neural Network (EBSNN) with long short-term memory (LSTM) for traffic classification across 20 websites, achieving 99.96% accuracy. They collected two kinds of datasets, application identification and website identification with 29 and 20 categories, respectively, and also chose a total of 11,607 flow records from 8 application categories for validation [14]. [PERSON] et al. defined five application traffic categories, Best-Effort/Default, Control Data, Video, IoT, and WebData, analyzing bandwidth requirements and QoS performance across 5G network slice scenarios based on service types and QoS-related characteristics [15]. [PERSON] et al. categorized video traffic into five groups based on QoS levels: broadcast video (BV), web video (WV), trade-style video (TSV), barter-style video (BSV), and interactive video (IV). Each category contained 100 flow samples, sourced from platforms such as Tudou, Youku, TVAnt, Skype, and others. Using a modified K-Singular Value Decomposition (KSVD) classification framework, they achieved 98.97% classification accuracy [16]. [PERSON] et al. employed a hierarchical k-Nearest Neighbor (k-NN) algorithm and achieved over 96.77% classification accuracy on their Internet video traffic dataset. This dataset included six categories: asymmetric standard-definition videos (ASD), asymmetric high-definition videos (AHD), HTTP-download videos (HDV), interactive videos (CV), P2P_video, and network live TV (ILV). It comprised 360 flow samples with a total data volume of 13.03 GB [17]. [PERSON] et al. developed a multimedia traffic classification system based on objective QoE, collecting 2741 video traffic flow samples labeled into five categories: Non-critical, Low-critical, Some-critical, Critical, and Very-critical [18]. [PERSON] et al. collected 400 peer-to-peer video streaming (P2 PTV) traffic samples and classified them into server-based bursty traffic, stable traffic, and peer-based bursty traffic [19]. [PERSON] et al. achieved an F1-score of 0.893 in classifying video traffic across two categories, Video-on-Demand (VoD) and live streaming, using a dataset of 1232 flow samples [12]. [PERSON] et al. defined the video category as LowVideo (Lv), AverageVideo (Av), and High-Video (Hv) based on the chance of detection of a potential characterization trained in flows fuzzy classifiers achieved as high as 90% classification accuracy; however, the datasets used in their study were not adequately described [20]. The above works have developed proprietary Internet video traffic datasets and provided in-depth analyses of video traffic. However, several issues persist. Video traffic often originates from different web platforms and applications or relies on privately emulated video traffic. This makes the classification of Internet video traffic into categories more subjective, as it depends on varying video traffic behaviors or attribute information. [PERSON] et al. enhanced Internet video traffic classification by introducing the SV category, further subdivided into Standard Definition (480P, SD)[PERSON] High Definition (720P, HD), and Ultra-clear Definition (1080P, UD), achieving 95% accuracy across seven categories using a Chain Hierarchical Structure (CHS) scheme. The dataset included 25,200 flow records [21][PERSON] In another study, [PERSON] et al. classified streaming video traffic based on raw video resolution, including audio, 144P, 240P, 360P, 480P, and 720P, into six categories. Using transport layer-encapsulated chunk data unit sizes as features with a Convolutional neural network (CNN) model, they achieved 99% accuracy for each category and a minimum F1-score of 95% on a dataset of 11,510 flow samples [22]. [PERSON] et al.