  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.
