An IDS-Based DNN Model Deployed on the Edge Network to Detect Industrial IoT Attacks
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-46749-3_29
Reference15 articles.
1. Mirani, A.A., Velasco-Hernandez, G., Awasthi, A., Walsh, J.: Key challenges and emerging technologies in industrial IoT architectures: a review. Sensors 22(15), 5836 (2022)
2. Mendonça, R.V., Silva, J.C., Rosa, R.L., Saadi, M., Rodriguez, D.Z., Farouk, A.: A lightweight intelligent intrusion detection system for industrial internet of things using deep learning algorithms. Expert. Syst. 39(5), e12917 (2022)
3. Corallo, A., Lazoi, M., Lezzi, M., Luperto, A.: Cybersecurity awareness in the context of the industrial internet of things: a systematic literature review. Comput. Ind. 137, 103614 (2022)
4. Cao, Y., Zhang, L., Zhao, X., Jin, K., Chen, Z.: An intrusion detection method for industrial control system based on machine learning. Information 13(7), 322 (2022)
5. Mokhtari, S., Abbaspour, A., Yen, K.K., Sargolzaei, A.: A machine learning approach for anomaly detection in industrial control systems based on measurement data. Electronics 10(4), 407 (2021)
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