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Silhouette - Identifying YouTube Video Flows from Encrypted Traffic

Silhouette - Identifying YouTube Video Flows from Encrypted Traffic


Feng Li, Jae Chung and Mark Claypool

In Proceedings of the 28th ACM International Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV)
Amsterdam, The Netherlands
June 15, 2018


Video streaming traffic often dominates mobile wireless networks, forcing Internet Service Providers (ISPs) to deploy video shaping to identify and then manage traffic during congested periods. Unfortunately, the increasing use of end-to-end encryption (e.g., TSL/SSL) makes it difficult to identify video flows even with deep packet inspection. As an alternative, this paper presents Silhouette - a real-time, lightweight video classification method suitable for ISP middle-boxes. Silhouette uses only flow statistics (i.e., "shape") for video identification making it payload-agnostic, effective for identifying video flow even when encrypted. Preliminary results with pre-classified YouTube traffic shows the promise of the Silhouette approach, yielding high identification accuracy over a range of video content and encoding qualities.


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