Automobile Driver Fingerprinting: A New Machine Learning Based Authentication Scheme
| Autor: | Yanning Zhang, Fang Yongqiang, Nei Kato, Jiajia Liu, Yijie Xun |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
business.industry
Computer science 020208 electrical & electronic engineering Real-time computing Iris recognition Automotive industry 02 engineering and technology Fingerprint recognition Computer Science Applications CAN bus Identification (information) Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering business Information Systems |
| Zdroj: | IEEE Transactions on Industrial Informatics. 16:1417-1426 |
| ISSN: | 1941-0050 1551-3203 |
| DOI: | 10.1109/tii.2019.2946626 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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