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An encrypted network video stream dataset.

Jan Fesl1,2, Daniel Sedlák1, Tomáš Macák2

  • 1Faculty of Information Technology, Department of Computer Systems, Czech Technical University in Prague.

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Summary
This summary is machine-generated.

Researchers created a dataset of video streams to identify content by analyzing unique data transmission patterns, or "fingerprints." This enables content and category identification for encrypted video streams from platforms like YouTube.

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Area of Science:

  • Computer Science
  • Network Security
  • Machine Learning

Background:

  • Online video streaming relies heavily on encrypted data transmission for user privacy.
  • Identifying specific video content within encrypted streams is challenging but valuable for network management and content analysis.
  • Previous work suggested that video streams possess unique, identifiable data transmission patterns (fingerprints).

Purpose of the Study:

  • To experimentally validate and leverage the concept of video stream 'fingerprinting' for content identification.
  • To create a comprehensive dataset of encrypted video streams from popular platforms for research purposes.
  • To enable the development of machine learning models for classifying encrypted video content.

Main Methods:

  • Collected a large dataset of video streams from PeerTube and YouTube over several months.
  • Captured network traffic data using probes during end-user playback.
  • Selected video streams thematically categorized for targeted analysis.

Main Results:

  • Demonstrated that video stream data transmissions exhibit non-constant, periodic patterns forming unique fingerprints.
  • Developed a dataset suitable for training machine learning algorithms and heuristic methods.
  • Established the feasibility of identifying video streams based on their content or category via network traffic analysis.

Conclusions:

  • Encrypted video streams can be identified by analyzing their unique data transmission fingerprints.
  • The created dataset facilitates the development of advanced algorithms for video stream classification.
  • This research contributes to understanding and managing encrypted video traffic in online streaming environments.