Interpretable Identification of Dynamic Adaptive Streaming over HTTP (DASH) Flows Based on Feature Engineering

Internet service providers allocate network resources for different network flows. Among them, video streaming requires substantial network bandwidth to provide a satisfactory user experience. The identification of video traffic is one of the tools that helps to manage and optimise network resources. However, available solutions usually focus on traffic traces from a single application and use black-box models for identification, which require labels for training. To address this issue, we proposed an unsupervised machine learning model to identify traffic generated by video applications from the three popular services, namely YouTube, Netflix, and Amazon Prime. Our methodology involves feature generation, filtering, and clustering. The clustering used the most significant features to group similar traffic patterns. We employed the following three algorithms that represent different clustering methodologies: partition-based, density-based, and probabilistic approaches. The clustering achieved precision between 0.78 and 0.93, while recall rates ranged from 0.68 to 0.84, depending on the experiment parameters, which is comparable with black-box learning models. The model presented is interpretable and scalable, which is useful for its practical application.