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Continuous Authentication of Automotive Vehicles Using Inertial Measurement Units.

Gianmarco Baldini1, Filip Geib1,2, Raimondo Giuliani1

  • 1European Commission, Joint Research Centre, 21027 Ispra, Italy.

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|December 6, 2019
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Summary
This summary is machine-generated.

This study introduces continuous vehicle authentication using Inertial Measurement Units (IMUs). Vehicle data accurately authenticates automotive vehicles, offering a novel alternative to traditional methods.

Keywords:
Inertial Measurement Unitsauthenticationroad transportationsecurity

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Area of Science:

  • Cybersecurity
  • Automotive Engineering
  • Signal Processing

Background:

  • Continuous Authentication (CA) verifies entities via ongoing digital output.
  • CA is established for personal authentication using wearable sensor data.
  • Vehicle authentication traditionally relies on cryptographic methods, limiting applications.

Purpose of the Study:

  • To explore the novel application of Continuous Authentication for automotive vehicles.
  • To investigate the feasibility of using Inertial Measurement Unit (IMU) data for vehicle authentication.
  • To compare different analytical methods for processing IMU data for authentication.

Main Methods:

  • Implementing Continuous Authentication using IMU data from vehicles during driving.
  • Extracting statistical features from time-domain IMU data.
  • Analyzing frequency-domain coefficients from IMU data.
  • Comparing performance across various road conditions and segments.

Main Results:

  • Demonstrated high accuracy in authenticating vehicles using IMU recordings.
  • Validated the effectiveness of CA for vehicles across different road segments.
  • Showcased the potential of IMU data for robust vehicle identification.

Conclusions:

  • Continuous Authentication using IMU data is a viable and accurate method for vehicle authentication.
  • This approach offers a promising alternative where cryptographic solutions are not feasible.
  • The study establishes a foundation for future research in intelligent vehicle security.