Trustworthy and Reliable Deep-Learning-Based Cyberattack Detection in Industrial IoT
- DOI
- 10.1109/tii.2022.3190352
- Published
- 2023-01
- Container
- IEEE Transactions on Industrial Informatics
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/tii.2022.3190352,
title = {Trustworthy and Reliable Deep-Learning-Based Cyberattack Detection in Industrial IoT},
author = {Fazlullah Khan and Ryan Alturki and Md Arafatur Rahman and Spyridon Mastorakis and Imran Razzak and Syed Tauhidullah Shah},
year = {2023},
journal = {IEEE Transactions on Industrial Informatics},
doi = {10.1109/tii.2022.3190352},
url = {https://doi.org/10.1109/tii.2022.3190352}
}RIS
TY - JOUR TI - Trustworthy and Reliable Deep-Learning-Based Cyberattack Detection in Industrial IoT AU - Fazlullah Khan AU - Ryan Alturki AU - Md Arafatur Rahman AU - Spyridon Mastorakis AU - Imran Razzak AU - Syed Tauhidullah Shah PY - 2023 JO - IEEE Transactions on Industrial Informatics DO - 10.1109/tii.2022.3190352 UR - https://doi.org/10.1109/tii.2022.3190352 ER -
APA
Khan, F., Alturki, R., Rahman, M. A., Mastorakis, S., Razzak, I., & Shah, S. T. (2023). Trustworthy and Reliable Deep-Learning-Based Cyberattack Detection in Industrial IoT. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/tii.2022.3190352
Source records
- crossref · retrieved 2026-11-10T14:53:28.265Z