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Forensic analysis of Twitch video streaming activities on Android.

Ali AlZahrani1, Mohamad Ahtisham Wani2, Wasim Ahmad Bhat1,2

  • 1Faculty of Computer & Information Systems, Islamic University of Madinah, Madinah, Saudi Arabia.

Journal of Forensic Sciences
|May 31, 2021
PubMed
Summary

This study explores proactive forensics for Twitch video streaming on Android devices. Researchers can recover evidence of user streaming activities, linking them to specific Twitch accounts for forensic investigations.

Keywords:
AndroidTwitchdigital forensicssmartphonestreamingvideo

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Area of Science:

  • Digital Forensics
  • Cybersecurity
  • Mobile Device Forensics

Background:

  • Video streaming platforms like Twitch are increasingly popular, presenting unique challenges for digital forensics.
  • Abuses on these platforms range from copyright infringement to severe criminal activities, necessitating effective investigative tools.

Purpose of the Study:

  • To develop and evaluate proactive forensic methods for analyzing Twitch video streaming activities on Android devices.
  • To identify and detail forensically significant artifacts related to Twitch usage on mobile platforms.

Main Methods:

  • Employed black-box testing principles for experimental design.
  • Utilized a specialized forensic framework on Android devices to capture and analyze data.
  • Focused on proactive data extraction and analysis of streaming activities.

Main Results:

  • Successfully demonstrated the extraction of evidence related to broadcast, shared, and watched streams.
  • Established a clear link between extracted forensic artifacts and specific Twitch user accounts.
  • Identified and documented key recoverable artifacts crucial for investigations.

Conclusions:

  • Forensic investigators can effectively uncover user-specific Twitch activities on Android devices.
  • The proposed methods and identified artifacts provide valuable tools for digital forensic investigations involving Twitch.
  • Proactive forensic analysis is feasible and effective for understanding mobile video streaming behaviors and potential abuses.