  Cooperative Adaptive Cruise Control (CACC) is an autonomous vehicle-following
technology that allows groups of vehicles on the highway to form in
tightly-coupled platoons. This is accomplished by exchanging inter-vehicle data
through Vehicle-to-Vehicle (V2V) wireless communication networks. CACC
increases traffic throughput and safety, and decreases fuel consumption.
However, the surge of vehicle connectivity has brought new security challenges
as vehicular networks increasingly serve as new access points for adversaries
trying to deteriorate the platooning performance or even cause collisions. In
this manuscript, we propose a novel attack detection scheme that leverage
real-time sensor/network data and physics-based mathematical models of vehicles
in the platoon. Nevertheless, even the best detection scheme could lead to
conservative detection results because of unavoidable modelling uncertainties,
network effects (delays, quantization, communication dropouts), and noise. It
is hard (often impossible) for any detector to distinguish between these
different perturbation sources and actual attack signals. This enables
adversaries to launch a range of attack strategies that can surpass the
detection scheme by hiding within the system uncertainty. Here, we provide risk
assessment tools (in terms of semidefinite programs) for Connected and
Automated Vehicles (CAVs) to quantify the potential effect of attacks that
remain hidden from the detector (referred here as \emph{stealthy attacks}). A
numerical case-study is presented to illustrate the effectiveness of our
methods.
