Automobile Driver Fingerprinting: A New Machine Learning Based Authentication Scheme

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

研究成果: Article査読

54 被引用数 (Scopus)

抄録

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.

本文言語English
論文番号8863987
ページ(範囲)1417-1426
ページ数10
ジャーナルIEEE Transactions on Industrial Informatics
16
2
DOI
出版ステータスPublished - 2020 2月

ASJC Scopus subject areas

  • 制御およびシステム工学
  • 情報システム
  • コンピュータ サイエンスの応用
  • 電子工学および電気工学

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