  Connected vehicle (CV) systems are cognizant of potential cyber attacks
because of increasing connectivity between its different components such as
vehicles, roadside infrastructure, and traffic management centers. However, it
is a challenge to detect security threats in real-time and develop appropriate
or effective countermeasures for a CV system because of the dynamic behavior of
such attacks, high computational power requirement, and a historical data
requirement for training detection models. To address these challenges,
statistical models, especially change point models, have potentials for
real-time anomaly detections. Thus, the objective of this study is to
investigate the efficacy of two change point models, Expectation Maximization
(EM) and two forms of Cumulative Summation (CUSUM) algorithms (i.e., typical
and adaptive), for real-time V2I cyber attack detection in a CV Environment. To
prove the efficacy of these models, we evaluated these two models for three
different type of cyber attack, denial of service (DOS), impersonation, and
false information, using basic safety messages (BSMs) generated from CVs
through simulation. Results from numerical analysis revealed that EM, CUSUM,
and adaptive CUSUM could detect these cyber attacks, DOS, impersonation, and
false information, with an accuracy of (99%, 100%, 100%), (98%, 10%, 100%), and
(100%, 98%, 100%) respectively.
