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  • Elsevier_C&S_accepted

    Rights statement: This is the author’s version of a work that was accepted for publication in Computers and Security. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers and Security, ?, ?, 2020 DOI: 10.1016/j.cose.2020.101917

    Accepted author manuscript, 449 KB, PDF document

    Embargo ends: 31/05/21

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

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Encrypted Video Traffic Clustering Demystified

Research output: Contribution to journalJournal article

E-pub ahead of print
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<mark>Journal publication date</mark>31/05/2020
<mark>Journal</mark>Computers and Security
Publication statusE-pub ahead of print
Early online date31/05/20
Original languageEnglish

Abstract

Cyber threat intelligence officers and forensics investigators often require the behavioural profiling of groups based on their online video viewing activity. 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. Nonetheless, composing such a subset is extremely challenging in real situations. Therefore, this work exhibits a novel profiling scheme for encrypted video traffic with no a priori assumption of a known subset of titles. It introduces a seminal synergy of Natural Language Processing (NLP) and Deep Encoder-based feature embedding algorithms with refined clustering schemes from off-the-shelf solutions, in order to group viewing profiles with unknown video streams. This study is the first to highlight the most computationally effective, accurate combinations of feature embedding and clustering using real datasets, thereby, paving the way to future forensics tools for automated behavioral profiling of malicious actors.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Computers and Security. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers and Security, ?, ?, 2020 DOI: 10.1016/j.cose.2020.101917