  While emerging adaptive cruise control (ACC) technologies are making their
way into more vehicles, they also expose a vulnerability to potential malicious
cyberattacks. Previous research has typically focused on constant or stochastic
attacks without explicitly addressing their malicious and covert
characteristics. As a result, these attacks may inadvertently benefit the
compromised vehicles, inconsistent with real-world scenarios. In contrast, we
establish an analytical framework to model and synthesize a range of candidate
attacks, offering a physical interpretation from the attacker's standpoint.
Specifically, we introduce a mathematical framework that describes mixed
traffic scenarios, comprising ACC vehicles and human-driven vehicles (HDVs),
grounded in car-following dynamics. Within this framework, we synthesize and
integrate a class of false data injection attacks into ACC sensor measurements,
influencing traffic flow dynamics. As a first-of-its-kind study, this work
provides an analytical characterization of attacks, emphasizing their malicious
and stealthy attributes while explicitly accounting for vehicle driving
behavior, thereby yielding a set of candidate attacks with physical
interpretability. To demonstrate the modeling process, we perform a series of
numerical simulations to holistically assess the effects of attacks on
car-following dynamics, traffic efficiency, and vehicular fuel consumption. The
primary findings indicate that strategically synthesized candidate attacks can
cause significant disruptions to the traffic flow while altering the driving
behavior of ACC vehicles in a subtle fashion to remain stealthy, which is
supported by a series of analytical results.
