Wireshark captures googlevideos7/9/2023 ![]() Nonetheless, composing such a subset is extremely challenging in real situations. It has been demonstrated that encrypted video traffic can be classified under the assumption of using a known subset of video titles based on temporal video viewing trends of particular groups. Based on a large number of offline and online traffic classification experiments, we demonstrate that it can independently classify, in real time, every video segment into one of the quality representation layers with 97.18% average accuracy.Ĭyber threat intelligence officers and forensics investigators often require the behavioural profiling of groups based on their online video viewing activity. We analyze the performance of this classification method with Safari over HTTPS. We present a new method and show for the first time that video quality representation classification for (YouTube) encrypted HTTP adaptive streaming is possible. These new methods will have to effectively estimate the quality representation layer and playout buffer. This highlights the need for new traffic classification methods for encrypted HTTP adaptive video streaming to enable smart traffic shaping. As a result, operators often encounter difficulties in shaping their encrypted video traffic via DPI. ![]() However, Google and certain content providers have started to encrypt their video services. ![]() The increasing popularity of HTTP adaptive video streaming services has dramatically increased bandwidth requirements on operator networks, which attempt to shape their traffic through Deep Packet Inspection (DPI). ![]()
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