Modern vehicles remain vulnerable to unauthorized use and theft despite traditional security measures including immobilizers and keyless entry systems. Criminals exploit vulnerabilities in Controller Area Network (CAN) bus systems to bypass authentication mechanisms, while social media trends have expanded auto theft to include recreational joyriding by underage drivers. Driver authentication via CAN bus data offers a promising additional layer of defense-in-depth protection, but existing open-access driver fingerprinting datasets suffer from critical limitations including reliance on decoded diagnostic data rather than raw CAN traffic, artificial fixed-route experimental designs, insufficient sampling rates, and lack of demographic information. This paper provides a comprehensive review of existing open-access driver fingerprinting datasets, analyzing their strengths and limitations to guide practitioners in dataset selection. We introduce the Kidmose CANid Dataset (KCID), which addresses these fundamental shortcomings by providing raw CAN bus data from 16 drivers across four vehicles, including essential demographic information and both daily driving and controlled fixed-route data. Beyond dataset contributions, we present a driver authentication anti-theft framework and implement a proof-of-concept prototype on a single-board computer. Through live road trials with an unaltered passenger vehicle, we demonstrate the practical feasibility of CAN bus-based driver authentication anti-theft systems. Finally, we explore diverse applications of KCID beyond driver authentication, including driver profiling for insurance and safety assessments, mechanical anomaly detection, young driver monitoring, and impaired driving detection. This work provides researchers with both the data and methodological foundation necessary to develop robust, deployable driver authentication systems...

翻译：尽管采用了包括防盗器和无钥匙进入系统在内的传统安全措施，现代车辆仍面临未经授权使用和盗窃的风险。犯罪分子利用控制器局域网（CAN）总线系统中的漏洞绕过认证机制，而社交媒体趋势更将汽车盗窃扩展至未成年人娱乐性兜风行为。通过CAN总线数据进行驾驶员认证为纵深防御体系提供了有前景的附加保护层，但现有开源驾驶员指纹数据集存在关键局限：依赖解码后的诊断数据而非原始CAN流量、采用人工固定路线实验设计、采样率不足以及缺乏人口统计信息。本文系统综述现有开源驾驶员指纹数据集，分析其优势与局限以指导实践者进行数据集选择。我们推出Kidmose CANid数据集（KCID），该数据集通过提供来自四辆车辆16位驾驶员的原始CAN总线数据（包含必要人口统计信息及日常驾驶与受控固定路线数据），从根本上解决了上述缺陷。除数据集贡献外，我们提出驾驶员认证防盗框架，并在单板计算机上实现概念验证原型。通过对未改装乘用车进行实际道路试验，我们证明了基于CAN总线的驾驶员认证防盗系统的实际可行性。最后，我们探索KCID在驾驶员认证之外的多元应用场景，包括保险与安全评估的驾驶员画像分析、机械异常检测、年轻驾驶员监控以及受损驾驶检测。本研究为研究人员开发稳健、可部署的驾驶员认证系统提供了必要的数据基础与方法论框架...