Spoofer detection framework for V2X systems via tensor-based DoA estimation and Yolo-based object detection
Published in IEEE Access, 2026
Da Silva, D. A., Da Silva, A. S., Lima, D. D., Da Costa, J. P., De Melo, L. O., Miranda, C., Santos, G. A., Vinel, A., Mendes, P., Verhoeven, S., Voigt-Antons, J.-N. & De Freitas, E. P.
This paper proposes a spoofer detection framework for vehicle-to-everything (V2X) communication systems. The approach combines tensor-based direction-of-arrival (DoA) estimation with YOLO-based object detection to identify inconsistencies between perceived and communicated vehicle positions. Experimental evaluation demonstrates the framework’s potential to enhance robustness and security in connected transportation environments.
Recommended citation: Da Silva, D. A., Da Silva, A. S., Lima, D. D., Da Costa, J. P., De Melo, L. O., Miranda, C., Santos, G. A., Vinel, A., Mendes, P., Verhoeven, S., Voigt-Antons, J.-N. & De Freitas, E. P. (2026). Spoofer detection framework for V2X systems via tensor-based DoA estimation and Yolo-based object detection. IEEE Access. https://doi.org/10.1109/ACCESS.2026.3660577