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JOURNAL[J33]
Spoofer detection framework for V2X systems via tensor-based DoA estimation and Yolo-based object detection
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.
IEEE Access
Abstract
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.
Record
- Reference
[J33]in the Publikationsverzeichnis - TypeJournal article
- Year2026
- Research lineQuality of Experience
- Identifier
2026-01-01-J33
BibTeX
@article{voigtantons2026j33,
author = {Da Silva, D. A. and Da Silva, A. S. and Lima, D. D. and Da Costa, J. P. and De Melo, L. O. and Miranda, C. and Santos, G. A. and Vinel, A. and Mendes, P. and Verhoeven, S. and Voigt-Antons, J.-N. and De Freitas, E. P.},
title = {Spoofer detection framework for V2X systems via tensor-based DoA estimation and Yolo-based object detection},
year = {2026},
journal = {IEEE Access},
doi = {10.1109/ACCESS.2026.3660577},
}