Intrusion Detection based on Privacy-preserving Federated Learning for the Industrial IoT-Reference-Cited by-同舟云学术

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Intrusion Detection based on Privacy-preserving Federated Learning for the Industrial IoT

Published:2021 Issue: Volume: Page:1-1
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Ruzafa-Alcazar Pedro,Fernandez-Saura Pablo,Marmol-Campos Enrique,Gonzalez-Vidal Aurora,Hernandez Ramos Jose Luis,Bernal Jorge,Skarmeta Antonio F.

Funder

Spanish Seneca Foundation CARM

European Regional Development Fund

European Commission

Ministerio de Ciencia e Innovacin

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/9424/4389054/09609643.pdf?arnumber=9609643

Cited by 61 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A contemporary survey of recent advances in federated learning: Taxonomies, applications, and challenges;Internet of Things;2024-10

2. Toward Ever-Evolution Network Threats: A Hierarchical Federated Class-Incremental Learning Approach for Network Intrusion Detection in IIoT;IEEE Internet of Things Journal;2024-09-15

3. A federated learning-based zero trust intrusion detection system for Internet of Things;Ad Hoc Networks;2024-09

4. Automated federated learning for intrusion detection of industrial control systems based on evolutionary neural architecture search;Computers & Security;2024-08

5. Against network attacks in renewable power plants: Malicious behavior defense for federated learning;Computer Networks;2024-08

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