AI-Driven Intrusion Detection for Vehicular Networks: A Comprehensive Survey of Techniques, Datasets, Deployment Architectures, and Future Directions

Syed Rizwan Hassan, Sadia Din, Muhammad Ismail Mohmand

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DOI: 10.3390/s26196211
Published: 2026-09-30
Container: Sensors
Publisher: MDPI AG
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.3390/s26196211,
  title = {AI-Driven Intrusion Detection for Vehicular Networks: A Comprehensive Survey of Techniques, Datasets, Deployment Architectures, and Future Directions},
  author = {Syed Rizwan Hassan and Sadia Din and Muhammad Ismail Mohmand},
  year = {2026},
  journal = {Sensors},
  doi = {10.3390/s26196211},
  url = {https://doi.org/10.3390/s26196211}
}

RIS

TY  - JOUR
TI  - AI-Driven Intrusion Detection for Vehicular Networks: A Comprehensive Survey of Techniques, Datasets, Deployment Architectures, and Future Directions
AU  - Syed Rizwan Hassan
AU  - Sadia Din
AU  - Muhammad Ismail Mohmand
PY  - 2026
JO  - Sensors
DO  - 10.3390/s26196211
UR  - https://doi.org/10.3390/s26196211
ER  -

APA

Hassan, S. R., Din, S., & Mohmand, M. I. (2026). AI-Driven Intrusion Detection for Vehicular Networks: A Comprehensive Survey of Techniques, Datasets, Deployment Architectures, and Future Directions. Sensors. https://doi.org/10.3390/s26196211

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crossref · retrieved 2026-11-04T21:44:05.089Z