Securing Connected & Autonomous Vehicles: Challenges posed by Adversarial Machine Learning and the way forward
- DOI
- 10.1088/1742-6596/1916/1/012110
- Published
- 2021-05-01
- Container
- Journal of Physics: Conference Series
- Publisher
- IOP Publishing
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1088/1742-6596/1916/1/012110,
title = {Securing Connected \& Autonomous Vehicles: Challenges posed by Adversarial Machine Learning and the way forward},
author = {S Kiruthika and A Aashin and K Gopinath and A Gowtham},
year = {2021},
journal = {Journal of Physics: Conference Series},
doi = {10.1088/1742-6596/1916/1/012110},
url = {https://doi.org/10.1088/1742-6596/1916/1/012110}
}RIS
TY - JOUR TI - Securing Connected & Autonomous Vehicles: Challenges posed by Adversarial Machine Learning and the way forward AU - S Kiruthika AU - A Aashin AU - K Gopinath AU - A Gowtham PY - 2021 JO - Journal of Physics: Conference Series DO - 10.1088/1742-6596/1916/1/012110 UR - https://doi.org/10.1088/1742-6596/1916/1/012110 ER -
APA
Kiruthika, S., Aashin, A., Gopinath, K., & Gowtham, A. (2021). Securing Connected & Autonomous Vehicles: Challenges posed by Adversarial Machine Learning and the way forward. Journal of Physics: Conference Series. https://doi.org/10.1088/1742-6596/1916/1/012110
Source records
- crossref · retrieved 2026-09-26T11:33:29.469Z