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Behavioral Acoustic Emanations: Attack and Verification of PIN Entry Using Keypress Sounds.

Sourav Panda1, Yuanzhen Liu2, Gerhard Petrus Hancke2

  • 1Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA.

Sensors (Basel, Switzerland)
|May 30, 2020
PubMed
Summary

This study reveals a low-cost acoustic side-channel attack that can recover 4-6 digit PINs with 60% accuracy. A machine-learning verification method can identify users with 88% accuracy, enhancing PIN entry device security.

Keywords:
PIN entry devicebiometric verificationpersonal identification numberside-channel attack

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Machine Learning

Background:

  • Personal Identification Number (PIN) entry devices (PEDs) are widely used for user authentication in various applications.
  • PEDs rely on small data strings like PINs for verifying user legitimacy.
  • Existing security measures are vulnerable to sophisticated attacks.

Purpose of the Study:

  • To explore the security vulnerabilities of PIN entry devices.
  • To propose a novel acoustic side-channel attack for PIN recovery.
  • To develop a machine learning-based user verification method to enhance security.

Main Methods:

  • Acoustic emanations from PIN entry devices were captured.
  • Keystroke intervals were extracted as features for machine learning models.
  • A PIN recovery attack model and a user verification model were trained.

Main Results:

  • The PIN recovery attack model achieved a 60% chance of successfully recovering a 4-6 digit PIN.
  • The user verification model demonstrated an 88% accuracy in identifying the correct user.
  • The acoustic side-channel attack was shown to be low-cost and effective.

Conclusions:

  • Acoustic side-channel attacks pose a significant threat to PIN entry devices.
  • The proposed verification method offers a viable countermeasure to improve PED security.
  • This research highlights the need for enhanced security protocols in user authentication systems.