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.

为了检测自动驾驶车辆遭受的网络攻击，本论文提出了一个基于交通模型框架和生成对抗网络的异常检测模型，通过实时车辆轨迹数据来识别恶意操作控制命令、传感器测量的虚假数据注入攻击以及拒绝服务攻击。与其他神经网络模型相比，所提出的方法在准确识别具有异常驾驶行为的自适应巡航控制车辆方面具有更高的准确率。