## 5 Conclusions  
Video traffic identification plays a crucial role in managing and securing computer networks. In this work, we proposed an ML model to distinguish traffic generated by file download, gaming, and social networking sessions from video traffic generated by popular services like YouTube, Netflix, and Amazon's Prime Video. In our approach, we generated features and filtered them selecting the most significant ones. Then, the selected features were passed to the clustering algorithms, which grouped similar traffic traces. For the feature generation, we analysed sliding windows with different lengths. Also, for comparison purposes, we employed three algorithms representing the following clustering approaches: partition-based, density-based, and probabilistic. Depending on the sliding window length and the algorithm, the clustering precision ranged from 0.78 to 0.93, while the recall ranged between 0.68 and 0.84. Considering individual flows from the non-video traffic, the process of multiple file downloads was the easiest to identify. The social media and gaming traffic were the hardest to identify due to their similarity. Of the video flows, the most distinguishable was Netflix-generated traffic. Nevertheless, the YouTube and Prime Video flows also achieved relatively good results. Moreover, the proposed solution achieves better results than the autoencoder ANN. This is probably due to the better use of a small amount of data by hypothesis-based feature filtering implemented in the tsfresh software. Of course, for both the proposed model and the ANN, there is room for optimization and changes in architecture or training algorithms. Our solution has several advantages. In contrast with most of the previous studies, we proposed clustering without a priori assumptions on a predefined set of network flow categories. Furthermore, our solution is not limited to video traces and can be applied to other network traffic, including data from different networks, protocols, or applications. As our approach is computationally less intensive than deep ML models, it is more scalable and can also handle large volumes of network traffic data from large-scale networks. As we used unsupervised ML, our approach can compete with labelled-based classification because it can automatically learn complex patterns and relationships in the data, rather than relying on predefined fingerprints or signatures. Furthermore, clustering algorithms can continuously learn and adapt to changes in the network traffic data over time. This can help ensure that the classification model remains accurate and up-to-date, even if the major providers upgrade their DASH implementation. The model presented is interpretable, which is vital in situations where transparency is critical. Network administrators need to understand why and how a specific traffic flow is classified into a certain category, as this knowledge enables them to make informed decisions and take appropriate actions. The ability to interpret the decisions made by ML models offers insights into the reasoning behind traffic classification outcomes and helps to build confidence in the model's performance. Additionally, interpretability aids in uncovering potential biases or limitations in the model's training data or architecture, facilitating necessary improvements and adjustments. Furthermore, the solution employs popular algorithm and their open-source implementation, which increases the chances of its practical implementation and reproducibility. The work was carried out within the statutory research project of the Silesian University of Technology, Department of Computer Networks and Systems. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. Data Availability Statement: Publicly available datasets were analysed in this study.