标题： CAN追踪攻击：利用CAN消息揭示驾驶轨迹 显示英文标题

标题： CAN-Trace Attack: Exploit CAN Messages to Uncover Driving Trajectories

摘要： 尽管有现有的缓解措施，驾驶轨迹数据仍然容易受到隐私泄露的威胁。 传统的检测驾驶轨迹的方法通常依赖于使用全球定位系统（GPS）数据对路径进行地图匹配，这容易受到GPS数据中断的影响。 本文介绍了CAN-Trace，这是一种新的隐私攻击机制，利用控制器局域网（CAN）消息来揭示驾驶轨迹，对驾驶员的长期隐私构成重大风险。 提出了一种新的轨迹重建算法，将CAN消息（特别是车辆速度和油门踏板位置）转换为适应各种驾驶状态的加权图。 CAN-Trace通过将创建的图与道路网络进行比较，应用图匹配算法来识别驾驶轨迹。 我们还设计了一种新的评估指标来评估匹配候选者，该指标允许存在潜在的数据缺失和匹配不准确性。 在不同现实条件下的实证验证，包括不同的车辆和驾驶区域，证明了CAN-Trace的有效性：在城市区域，攻击成功率达到90.59%，在郊区区域达到99.41%。 显示英文摘要

摘要： Driving trajectory data remains vulnerable to privacy breaches despite existing mitigation measures. Traditional methods for detecting driving trajectories typically rely on map-matching the path using Global Positioning System (GPS) data, which is susceptible to GPS data outage. This paper introduces CAN-Trace, a novel privacy attack mechanism that leverages Controller Area Network (CAN) messages to uncover driving trajectories, posing a significant risk to drivers' long-term privacy. A new trajectory reconstruction algorithm is proposed to transform the CAN messages, specifically vehicle speed and accelerator pedal position, into weighted graphs accommodating various driving statuses. CAN-Trace identifies driving trajectories using graph-matching algorithms applied to the created graphs in comparison to road networks. We also design a new metric to evaluate matched candidates, which allows for potential data gaps and matching inaccuracies. Empirical validation under various real-world conditions, encompassing different vehicles and driving regions, demonstrates the efficacy of CAN-Trace: it achieves an attack success rate of up to 90.59% in the urban region, and 99.41% in the suburban region.