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Automobile Driver Fingerprinting: A New Machine Learning Based Authentication Scheme

机译:汽车驱动器指纹:基于新的机器学习认证方案

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摘要

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.
机译:高级技术在汽车工业中不断出现,这不仅为驾驶员提供了舒适的驾驶体验,而且还提高了乘客的安全性。但是,仍有一些安全问题需要在汽车中解决,例如汽车司机指纹识别。目前,识别技术,例如指纹识别和虹膜识别,无法以实时方式监视驾驶员的身份。因此,设计实时汽车司机指纹识别方案是具有重要意义,以确保人们的性质甚至生命的安全性。与以往的汽车司机指纹识别有关的不同,在本文中,我们对两个车辆的司机的行为特征进行了全面的研究,即Luxgen U5 SUV和Buick Regal。我们利用控制器区域网络的实际数据通过提取和处理特征数据来构造驱动程序标识比较库。然后,我们构建基于卷积神经网络的组合模型,支持矢量域描述以实现高效的汽车驾驶员指纹识别。广泛的实验结果表明,所提出的驱动器指纹识别方案可以实时动态地匹配驾驶员的身份,而不会影响正常驾驶。

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