DAICS: A Deep Learning Solution for Anomaly Detection in Industrial Control Systems-Reference-Cited by-同舟云学术

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DAICS: A Deep Learning Solution for Anomaly Detection in Industrial Control Systems

Published:2021 Issue: Volume: Page:1-1
ISSN:2168-6750
Container-title:IEEE Transactions on Emerging Topics in Computing
language:
Short-container-title:IEEE Trans. Emerg. Topics Comput.

Author:

Abdelaty Maged Fathy¹,Doriguzzi Corin Roberto²,Siracusa Domenico³

Affiliation:

1. RiSING Research Unit, Fondazione Bruno Kessler, 18466 Trento, Trento, Italy, (e-mail: mabdelaty@fbk.eu)

2. Cybersecurity, Fondazione Bruno Kessler, 18466 Trento, Trento, Italy, 38123 (e-mail: rdoriguzzi@fbk.eu)

3. RiSING Research Unit, Fondazione Bruno Kessler, 18466 Trento, Trento, Italy, (e-mail: dsiracusa@fbk.eu)

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Human-Computer Interaction,Information Systems,Computer Science (miscellaneous)

Link

http://xplorestaging.ieee.org/ielx7/6245516/6558478/09403989.pdf?arnumber=9403989

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1. A real-time network based anomaly detection in industrial control systems;International Journal of Critical Infrastructure Protection;2024-07

2. A novel approach detection for IIoT attacks via artificial intelligence;Cluster Computing;2024-05-06

3. An improved autoencoder-based approach for anomaly detection in industrial control systems;Systems Science & Control Engineering;2024-04-18

4. An explainable multi-modal model for advanced cyber-attack detection in industrial control systems;Internet of Things;2024-04

5. An Ontology-Based Cybersecurity Framework for AI-Enabled Systems and Applications;Future Internet;2024-02-22

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