Evaluating Filter, Wrapper, and Embedded Feature Selection Approaches for Encrypted Video Traffic Classification

Classification of video traffic is crucial for network management, enforcing quality of service, and optimising bandwidth. Feature selection plays a vital role in traffic identification by reducing data volume, enhancing accuracy, and reducing computational cost. This paper presents a comparative study of three feature selection approaches applied to video traffic identification: filter, wrapper, and embedded. Real-world traffic traces are collected from three popular video streaming platforms: YouTube, Netflix, and Amazon Prime Video, representing diverse content delivery characteristics. The main contributions of this work are (1) the identification of traffic generated by these streaming services, (2) a comparative evaluation of three feature selection methods, and (3) the application of previously untested algorithms for this task. We evaluate the examined methods using F1-score and computational efficiency. The results demonstrate distinct trade-offs among the approaches: the filter method offers low computational overhead with moderate accuracy, while the wrapper method achieves higher accuracy at the cost of longer processing times. The embedded method provides a balanced compromise by integrating feature selection within model training. This comparative analysis offers insights for designing video traffic identification systems in modern heterogeneous networks.