TY - JOUR
T1 - Automobile Driver Fingerprinting
T2 - A New Machine Learning Based Authentication Scheme
AU - Xun, Yijie
AU - Liu, Jiajia
AU - Kato, Nei
AU - Fang, Yongqiang
AU - Zhang, Yanning
N1 - Publisher Copyright:
© 2005-2012 IEEE.
PY - 2020/2
Y1 - 2020/2
N2 - 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.
AB - 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.
KW - Convolutional neural network (CNN)
KW - driver fingerprinting
KW - driver identification
KW - illegal driver detection
KW - machine learning
KW - support vector domain description (SVDD)
UR - http://www.scopus.com/inward/record.url?scp=85078699960&partnerID=8YFLogxK
U2 - 10.1109/TII.2019.2946626
DO - 10.1109/TII.2019.2946626
M3 - 文章
AN - SCOPUS:85078699960
SN - 1551-3203
VL - 16
SP - 1417
EP - 1426
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
IS - 2
M1 - 8863987
ER -