Detecting Malicious Anomalies in Heavy-Duty Vehicular Networks Using Long Short-Term Memory Models
- DOI
- 10.3390/s25144430
- Published
- 2025-07-16
- Container
- Sensors
- Publisher
- MDPI AG
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3390/s25144430,
title = {Detecting Malicious Anomalies in Heavy-Duty Vehicular Networks Using Long Short-Term Memory Models},
author = {Mark J. Potvin and Sylvain P. Leblanc},
year = {2025},
journal = {Sensors},
doi = {10.3390/s25144430},
url = {https://doi.org/10.3390/s25144430}
}RIS
TY - JOUR TI - Detecting Malicious Anomalies in Heavy-Duty Vehicular Networks Using Long Short-Term Memory Models AU - Mark J. Potvin AU - Sylvain P. Leblanc PY - 2025 JO - Sensors DO - 10.3390/s25144430 UR - https://doi.org/10.3390/s25144430 ER -
APA
Potvin, M. J., & Leblanc, S. P. (2025). Detecting Malicious Anomalies in Heavy-Duty Vehicular Networks Using Long Short-Term Memory Models. Sensors. https://doi.org/10.3390/s25144430
Source records
- crossref · retrieved 2026-09-27T04:44:51.713Z