Connected vehicles (CVs), because of the external connectivity with other CVs
and connected infrastructure, are vulnerable to cyberattacks that can instantly
compromise the safety of the vehicle itself and other connected vehicles and
roadway infrastructure. One such cyberattack is the false information attack,
where an external attacker injects inaccurate information into the connected
vehicles and eventually can cause catastrophic consequences by compromising
safety-critical applications like the forward collision warning. The occurrence
and target of such attack events can be very dynamic, making real-time and
near-real-time detection challenging. Change point models, can be used for
real-time anomaly detection caused by the false information attack. In this
paper, we have evaluated three change point-based statistical models;
Expectation Maximization, Cumulative Summation, and Bayesian Online Change
Point Algorithms for cyberattack detection in the CV data. Also, data-driven
artificial intelligence (AI) models, which can be used to detect known and
unknown underlying patterns in the dataset, have the potential of detecting a
real-time anomaly in the CV data. We have used six AI models to detect false
information attacks and compared the performance for detecting the attacks with
our developed change point models. Our study shows that change points models
performed better in real-time false information attack detection compared to
the performance of the AI models. Change point models having the advantage of
no training requirements can be a feasible and computationally efficient
alternative to AI models for false information attack detection in connected

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