  Vehicular communication networks are rapidly emerging as vehicles become
smarter. However, these networks are increasingly susceptible to various
attacks. The situation is exacerbated by the rise in automated vehicles
complicates, emphasizing the need for security and authentication measures to
ensure safe and effective traffic management. In this paper, we propose a novel
hybrid physical layer security (PLS)-machine learning (ML) authentication
scheme by exploiting the position of the transmitter vehicle as a device
fingerprint. We use a time-of-arrival (ToA) based localization mechanism where
the ToA is estimated at roadside units (RSUs), and the coordinates of the
transmitter vehicle are extracted at the base station (BS).Furthermore, to
track the mobility of the moving legitimate vehicle, we use ML model trained on
several system parameters. We try two ML models for this purpose, i.e., support
vector regression and decision tree. To evaluate our scheme, we conduct binary
hypothesis testing on the estimated positions with the help of the ground
truths provided by the ML model, which classifies the transmitter node as
legitimate or malicious. Moreover, we consider the probability of false alarm
and the probability of missed detection as performance metrics resulting from
the binary hypothesis testing, and mean absolute error (MAE), mean square error
(MSE), and coefficient of determination $\text{R}^2$ to further evaluate the ML
models. We also compare our scheme with a baseline scheme that exploits the
angle of arrival at RSUs for authentication. We observe that our proposed
position-based mechanism outperforms the baseline scheme significantly in terms
of missed detections.
