Considering the exponential growth of network traffic, particularly driven by over-the-top (OTT) streaming applications, video category network traffic constitutes a significant portion of overall network traffic. However, most research has focused on the categorization and diversity of network traffic using benchmark datasets, with limited attention paid to video category network traffic. Additionally, there is a lack of proprietary Internet video traffic datasets, and the few proprietary datasets available often lack transparency and interpretability. This paper introduces a novel framework for generating proprietary Internet video traffic datasets, addressing existing gaps in dataset quality and consistency. We propose the nYFTQC algorithm, which enables the creation of fifteen detailed datasets specifically designed for Internet video traffic analysis. The proposed datasets demonstrate superior performance metrics, including completeness, consistency, and transparency. This comprehensive approach enhances the accuracy and interpretability of traffic sample analysis, providing valuable resources for future research in video category network traffic.

Promocijas darba mērķis ir panākt interaktīvo multimediju lietotņu ģenerētās tīkla trafika smalkgraudainu uzraudzību un klasifikāciju dažādos tīkla apstākļos. Klasifikācija tiek veikta trīs detalizācijas līmeņos – laika rindas, plūsmas un datu nesēja (payload) līmenī. Šī pieeja veicina padziļinātu izpratni par interaktīvā multimediju trafika raksturlielumiem un to nozīmi sarežģītās tīkla vidēs. Darbs sniedz ieguldījumu arī trafika uzvedības modelēšanā tīkla trafika klasifikācijas (NTC) uzdevumiem un tiek paplašināts plašākiem tīkla trafika uzraudzības un analīzes (NTMA) lietojumiem.

The Doctoral Thesis aims to achieve fine-grained monitoring and classification of network traffic generated by interactive media applications across diverse network conditions. The classification is conducted at three granularity levels: time series, flow, and payload. This approach enhances understanding of interactive media traffic patterns and their roles in complex network environments. It also contributes to traffic behaviour modelling for Network Traffic Classification (NTC) tasks and extends to broader Network Traffic Monitoring and Analysis (NTMA) applications.