Automobile Driver Fingerprinting: A New Machine Learning Based Authentication Scheme

Yijie Xun, Jiajia Liu, Nei Kato, Yongqiang Fang, Yanning Zhang

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Advanced technologies are constantly emerging in automobile industry, which not only provides drivers with a comfortable driving experience, but also enhances the safety of passengers. However, there are still some security issues need to be solved in automobiles, such as automobile driver fingerprinting. At present, identification technologies, such as fingerprint recognition and iris recognition, cannot monitor the driver's identity in real-time manner. Therefore, it is of great significance to design a real-time automobile driver fingerprinting scheme to ensure the safety of people's properties and even lives. Different from previous work concerning automobile driver fingerprinting, in this article, we conduct a comprehensive study on behavioral characteristics of drivers in two vehicles, namely Luxgen U5 SUV and Buick Regal. We exploit the actual data of the controller area network to construct a driver identity comparison library by extracting and processing the feature data. Then, we construct a combined model based on convolutional neural network and support vector domain description to achieve efficient automobile driver fingerprinting. Extensive experimental results show that the proposed driver fingerprinting scheme can dynamically match the driver's identity in real time without affecting the normal driving.

Original languageEnglish
Article number8863987
Pages (from-to)1417-1426
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Volume16
Issue number2
DOIs
Publication statusPublished - 2020 Feb

Keywords

  • Convolutional neural network (CNN)
  • driver fingerprinting
  • driver identification
  • illegal driver detection
  • machine learning
  • support vector domain description (SVDD)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

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