Clustering the Unknown - The Youtube Case

Recent stringent end-user security and privacy requirements caused the dramatic rise of encrypted video streams in which YouTube encrypted traffic is one of the most prevalent. Regardless of their encrypted nature, metadata derived from such traffic flows can be utilized to identify the title of a video, thus enabling the classification of video streams into a single video title using a given video title set. Nonetheless, scenarios where no video title set is present and a supervised approach is not feasible, are both frequent and challenging. In this paper we go beyond previous studies and demonstrate the feasibility of clustering unknown video streams into subgroups although no information is available about the title name. We address this problem by exploring Natural Language Processing (NLP) formulations and Word2vec techniques to compose a novel statistical feature in order to further cluster unknown video streams. Through our experimental results over real datasets we demonstrate that our methodology is capable to cluster 72 video titles out of 100 video titles from a dataset of 10,000 video streams. Thus, we argue that the proposed methodology could sufficiently contribute to the newly rising and demanding domain of encrypted Internet traffic classification.