English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Vehicular communication networks are rapidly emerging as vehicles becomesmarter. However, these networks are increasingly susceptible to variousattacks. The situation is exacerbated by the rise in automated vehiclescomplicates, emphasizing the need for security and authentication measures toensure safe and effective traffic management. In this paper, we propose a novelhybrid physical layer security (PLS)-machine learning (ML) authenticationscheme by exploiting the position of the transmitter vehicle as a devicefingerprint. We use a time-of-arrival (ToA) based localization mechanism wherethe ToA is estimated at roadside units (RSUs), and the coordinates of thetransmitter vehicle are extracted at the base station (BS).Furthermore, totrack the mobility of the moving legitimate vehicle, we use ML model trained onseveral system parameters. We try two ML models for this purpose, i.e., supportvector regression and decision tree. To evaluate our scheme, we conduct binaryhypothesis testing on the estimated positions with the help of the groundtruths provided by the ML model, which classifies the transmitter node aslegitimate or malicious. Moreover, we consider the probability of false alarmand the probability of missed detection as performance metrics resulting fromthe binary hypothesis testing, and mean absolute error (MAE), mean square error(MSE), and coefficient of determination $\text{R}^2$ to further evaluate the MLmodels. We also compare our scheme with a baseline scheme that exploits theangle of arrival at RSUs for authentication. We observe that our proposedposition-based mechanism outperforms the baseline scheme significantly in termsof missed detections.

Hybrid PLS-ML Authentication Scheme for V2I Communication Networks