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Area of Science:

  • Cybersecurity
  • Signal Processing
  • Machine Learning

Background:

  • Device-to-device authentication leverages manufacturing variations in microphones and speakers.
  • Previous methods focused on multi-class recognition, unsuitable for large, unknown device pools.
  • Real-world authentication requires recognizing a single device from numerous untracked devices.

Purpose of the Study:

  • To develop a robust device-to-device authentication system using acoustic fingerprints.
  • To implement one-class classification for recognizing a single legitimate device.
  • To explore cloud-based deployment for efficient computation and data storage.

Main Methods:

  • Utilized one-class classification algorithms: one-class Support Vector Machine and Local Outlier Factor.
  • Trained models on acoustic fingerprints derived from manufacturing variations.
  • Deployed and tested the system on smartphones and an automotive headunit.

Main Results:

  • Achieved recognition rates between 50% and 100% across various devices.
  • Evaluated performance under diverse environmental conditions: distance, altitude, and component aging.
  • Demonstrated the feasibility of cloud-based processing for authentication tasks.

Conclusions:

  • One-class classification effectively enables single-device recognition from large pools using acoustic fingerprints.
  • The proposed system shows promise for secure authentication in various environments, including in-vehicle systems.
  • Environmental factors and component aging present challenges, but solutions are proposed within the threat model.