# A Novel Proprietary Internet Video Traffic Dataset Generation Algorithm
## 2 Related Works  
In this section, we will comprehensively compare the characteristics of video-type traffic across various public and proprietary network traffic datasets, including category and application selection, transmission protocol, sample size ratio, etc. The BRASIL dataset contains a total of 14 categories, with over one million traffic flow records; however, multimedia traffic accounts for less than 0.1% [2]. The ISCXVPN2016 and ISCXTor2016 datasets categorize traffic into 14 groups based on VPN and Tor characteristics, respectively. Each dataset includes seven main categories, with the video category featuring streaming applications such as Vimeo, Facebook, and YouTube. Both ISCXVPN2016 and ISCXTor2016 contain over 150,000 flow samples, with video-streaming traffic accounting for less than 5% and 2%, respectively [3; 4]. The CIC-Darknet2020 dataset merges the VPN and Tor traffic from the ISCXVPN2016 and ISCXTor2016 datasets into a Darknet category, with video-streaming traffic still accounting for less than 5% of over 150,000 flow samples [5]. The traffic categories in these datasets are often selected based on specific tasks in the NTMC, such as intrusion detection, botnet detection, encrypted traffic analysis, and anonymized network traffic studies. [PERSON] et al. developed the OSF-EIMTC traffic classification framework, which demonstrated excellent performance and was validated on several public and benchmark datasets, including USTC-TFC2016, ISCXVPN2016, Ariel (BOA2016), MAppGraph2021, MTA (Malware Traffic Analysis), and StratosphereIPS [6]. The USTC-TFC2016 dataset includes 10 categories of malware traffic and 10 categories of benign traffic, with the video-related category containing applications such as FaceTime and Skype. This dataset includes over 75,000 flow and session samples [7]. The StratosphereIPS dataset includes only three categories, benign, malware, and mixed, without application or protocol metadata. The MTA dataset contains two categories, malware and legitimate traffic, both lacking application and protocol metadata. The Ariel dataset contains over 20,000 session samples across 30 categories, with video traffic from YouTube and Facebook accounting for less than 10% of the total dataset [8]. The MAppGraph dataset provides over one million flow records spanning 101 mobile application categories. Its video traffic category includes applications such as Netflix, TikTok, and Twitch, which contribute less than 10% of the total dataset [9]. [PERSON] et al. proposed the Multi-Task Learning Fusion (MTFU) algorithm, achieving 94.67% classification accuracy on the QUIC dataset, which includes five service categories: Google Docs, Google Drive, Google Music, YouTube, and Google Search. The dataset comprises nearly 7000 flow records [10]. Existing studies show that video traffic samples make up only a small portion of most public and benchmark datasets. Key issues include limited sample sizes, narrowly defined categories, and a lack of metadata descriptions. Additionally, these datasets often employ inconsistent naming conventions for applications, categories, SSL server names, Server Name Indication (SNI), and other elements, complicating the validation of relationships between samples and their naming conventions. Some proprietary Internet traffic datasets focus on the video traffic category. [PERSON] et al. utilized packet-level and flow-level features such as size, inter-arrival time, and packet flow direction, combined with ensemble deep learning classifiers, achieving 89.77% classification accuracy across four traffic categories: Interactive, Bulk Data Transfer, Streaming, and Transaction. Their dataset included 3612 flow records [11]. [PERSON] et al. categorized video traffic into six groups based on Quality of Service (QoS) and Quality of Experience(QoE) characteristics: video streaming, web browsing, social networking, audio communication, text communication, and bulk download. Their dataset included 7154 session samples [12]. [PERSON] et al.