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Related Experiment Video

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A Forensically Sound Adversary Model for Mobile Devices.

Quang Do1, Ben Martini1, Kim-Kwang Raymond Choo1

  • 1Information Assurance Research Group, University of South Australia, Adelaide, Australia.

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This study introduces an adaptable adversary model for mobile device forensics, ensuring forensic soundness. The model successfully extracted crucial data from Android devices and cloud apps, aiding investigations.

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Area of Science:

  • Digital Forensics
  • Cybersecurity
  • Mobile Computing

Background:

  • Mobile devices are rapidly evolving, posing challenges for digital forensic investigations.
  • Maintaining forensic soundness is a critical constraint for forensic practitioners.
  • Existing forensic methodologies may not adequately address the dynamic nature of mobile technologies.

Purpose of the Study:

  • To propose a novel adversary model for mobile device forensics.
  • To ensure the adversary model is adaptable to new mobile technologies.
  • To integrate the principle of forensic soundness into the adversary model.

Main Methods:

  • Developed an adversary model tailored for mobile device forensics.
  • Constructed an evidence collection and analysis methodology for Android devices.
  • Applied the methodology to six popular cloud applications on Android devices.

Main Results:

  • Successfully extracted forensically relevant information from Android devices.
  • Data was retrieved from both external and internal storage.
  • The model demonstrated adaptability to current mobile device technologies.

Conclusions:

  • The proposed adversary model facilitates mobile device forensic investigations.
  • The model effectively incorporates forensic soundness constraints.
  • The methodology shows promise for extracting data from cloud apps on mobile devices.