Optimized deep autoencoder and BiLSTM for intrusion detection in IoTs-Fog computing
Author:
Funder
Prince Sattam bin Abdulaziz University
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11042-024-18919-0.pdf
Reference55 articles.
1. Rani M, Gagandeep (2022) Effective network intrusion detection by addressing class imbalance with deep neural networks multimedia tools and applications. Multimed Tools Applic 81(6):8499–8518
2. Samy A, Yu H, Zhang H (2020) Fog-based attack detection framework for internet of things using deep learning. IEEE Access 8:74571–74585
3. Hazman C, Guezzaz A, Benkirane S, Azrour M (2023) Toward an intrusion detection model for IoT-based smart environments. Multimed Tools Applic 1–22. https://doi.org/10.1007/s11042-023-16436-0
4. Vinayakumar, Ravi et al (2019) Deep learning approach for intelligent intrusion detection system. IEEE Access 7:41525–41550
5. Pustokhina IV, Pustokhin DA, Lydia EL, Garg P, Kadian A, Shankar K (2022) Hyperparameter search based convolution neural network with Bi-LSTM model for intrusion detection system in multimedia big data environment. Multimed Tools Applic 81(24):34951–34968
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