A Hybrid Deep Autoencoders and Random Forest Framework for False Data Injection Attack Detection in Industrial Internet of Things Networks
- DOI
- 10.3390/s26165110
- Published
- 2026-08-12
- Container
- Sensors
- Publisher
- MDPI AG
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3390/s26165110,
title = {A Hybrid Deep Autoencoders and Random Forest Framework for False Data Injection Attack Detection in Industrial Internet of Things Networks},
author = {Abdullah M. Albarrak and Fuad A. Ghaleb and Sultan Noman Qasem and Faisal Saeed},
year = {2026},
journal = {Sensors},
doi = {10.3390/s26165110},
url = {https://doi.org/10.3390/s26165110}
}RIS
TY - JOUR TI - A Hybrid Deep Autoencoders and Random Forest Framework for False Data Injection Attack Detection in Industrial Internet of Things Networks AU - Abdullah M. Albarrak AU - Fuad A. Ghaleb AU - Sultan Noman Qasem AU - Faisal Saeed PY - 2026 JO - Sensors DO - 10.3390/s26165110 UR - https://doi.org/10.3390/s26165110 ER -
APA
Albarrak, A. M., Ghaleb, F. A., Qasem, S. N., & Saeed, F. (2026). A Hybrid Deep Autoencoders and Random Forest Framework for False Data Injection Attack Detection in Industrial Internet of Things Networks. Sensors. https://doi.org/10.3390/s26165110
Source records
- crossref · retrieved 2026-11-10T08:45:33.985Z