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SegAuth: A Segment-based Approach to Behavioral Biometric Authentication.

Yanyan Li1, Mengjun Xie1, Jiang Bian2

  • 1University of Arkansas at Little Rock.

... IEEE Conference on Communications and Network Security. IEEE Conference on Communications and Network Security
|June 3, 2017
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Summary
This summary is machine-generated.

This study introduces SegAuth, a novel behavioral biometric authentication method using gesture segments for improved mobile device security. SegAuth enhances verification accuracy by analyzing distinctive, repetitive gesture patterns.

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Area of Science:

  • Computer Science
  • Biometrics
  • Human-Computer Interaction

Background:

  • Behavioral biometric authentication on mobile devices shows promise but faces accuracy concerns due to the dynamic nature of user behavior.
  • Existing methods often struggle with the variability inherent in behavioral biometrics, limiting their reliability.

Purpose of the Study:

  • To address the accuracy limitations of behavioral biometrics by proposing a new authentication method.
  • To introduce SegAuth, a system that utilizes segments of gestures rather than entire gestures for more robust authentication.

Main Methods:

  • Proposed SegAuth, a behavioral biometric authentication method analyzing gesture segments.
  • Transformed time-series data from gestures into string tokens, prioritizing distinctive and repetitive segments.
  • Calculated an overall genuine score based on these tokens to determine user authenticity.

Main Results:

  • SegAuth demonstrated high accuracy across four diverse datasets.
  • The method consistently outperformed existing popular behavioral biometric authentication techniques.
  • Analysis of distinctive gesture segments proved effective in enhancing verification accuracy.

Conclusions:

  • SegAuth offers a promising approach to overcome the accuracy challenges in behavioral biometrics.
  • The segment-based analysis provides a more reliable method for user authentication on mobile devices.
  • This technique can be applied to various gesture and motion-based authentication scenarios.