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Touch events and human activities for continuous authentication via smartphone.

Vincenzo Gattulli1, Donato Impedovo2, Giuseppe Pirlo2

  • 1Dipartimento di Informatica, Università degli studi di Bari Aldo Moro, 70125, Bari, Italy. Vincenzo.Gattulli@Uniba.It.

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Summary

Continuous authentication using touch events and smartphone sensors significantly enhances smartphone security. This method, leveraging machine learning, achieves high accuracy for user verification during document scrolling.

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Human-Computer Interaction

Background:

  • Modern smartphone security relies on continuous authentication methods.
  • Touch events and human activities provide silent, rich data for machine learning.
  • Existing methods require user interaction, impacting user experience.

Purpose of the Study:

  • To develop a continuous authentication method for smartphones.
  • To authenticate users while they are sitting and scrolling documents.
  • To evaluate the effectiveness of machine learning models using sensor data.

Main Methods:

  • Utilized touch events and smartphone sensor features from the H-MOG Dataset.
  • Incorporated the Signal Vector Magnitude feature for each sensor.
  • Evaluated several machine learning models using 1-class and 2-class experiment setups.

Main Results:

  • The 1-class Support Vector Machine (SVM) model achieved 98.9% accuracy.
  • The 1-class SVM model achieved an F1-score of 99.4%.
  • The Signal Vector Magnitude feature was identified as highly significant.

Conclusions:

  • Continuous authentication using touch events and sensor data is highly effective.
  • Machine learning models, particularly 1-class SVM, offer robust smartphone security.
  • The proposed method provides a silent and efficient authentication solution.