With the advent of vehicles equipped with advanced driver-assistance systems, such as adaptive cruise control (ACC) and other automated driving features, the potential for cyberattacks on these automated vehicles (AVs) has emerged. While overt attacks that force vehicles to collide may be easily identified, more insidious attacks, which only slightly alter driving behavior, can result in network-wide increases in congestion, fuel consumption, and even crash risk without being easily detected. To address the detection of such attacks, we first present a traffic model framework for three types of potential cyberattacks: malicious manipulation of vehicle control commands, false data injection attacks on sensor measurements, and denial-of-service (DoS) attacks. We then investigate the impacts of these attacks at both the individual vehicle (micro) and traffic flow (macro) levels. A novel generative adversarial network (GAN)-based anomaly detection model is proposed for real-time identification of such attacks using vehicle trajectory data. We provide numerical evidence {to demonstrate} the efficacy of our machine learning approach in detecting cyberattacks on ACC-equipped vehicles. The proposed method is compared against some recently proposed neural network models and observed to have higher accuracy in identifying anomalous driving behaviors of ACC vehicles.

翻译：随着配备自适应巡航控制（ACC）及其他自动驾驶功能的先进驾驶辅助系统车辆的出现，针对这些自动驾驶汽车（AV）的网络攻击可能性随之显现。虽然迫使车辆碰撞的显式攻击易于识别，但更隐蔽的攻击（仅轻微改变驾驶行为）可能导致网络范围交通拥堵加剧、燃油消耗增加，甚至碰撞风险上升，且不易被察觉。为了解决此类攻击的检测问题，我们首先提出了一种针对三种潜在网络攻击的交通模型框架：对车辆控制指令的恶意篡改、针对传感器测量的虚假数据注入攻击，以及拒绝服务（DoS）攻击。随后，我们在单车（微观）和交通流（宏观）两个层面研究了这些攻击的影响。提出了一种基于生成对抗网络（GAN）的新型异常检测模型，用于利用车辆轨迹数据实时识别此类攻击。我们提供了数值证据，证明所提出的机器学习方法在检测ACC车辆网络攻击方面的有效性。将所提方法与近期提出的几种神经网络模型进行对比，发现其在识别ACC车辆异常驾驶行为方面具有更高的准确性。