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IDG-SemiAD: An Immune Detector Generation-Based Collaborative Learning Scheme for Semi-supervised Anomaly Detection in Industrial Cyber-physical Systems

Crossref DOI link: https://doi.org/10.1007/s44196-024-00498-5

Published Online: 2024-04-29

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Huang, Meng

Li, Wenshan

He, Junjiang

Lan, Xiaolong

Li, Tao

Zhang, Nian
Funding

Funding for this research was provided by:

Key Technologies Research and Development Program (2020YFB1805400)

National Natural Science Foundation of China (62032002)

National Natural Science Foundation of China (62101358)
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Text and Data Mining valid from 2024-04-29

Version of Record valid from 2024-04-29
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Article History

Received: 15 January 2024

Accepted: 3 April 2024

First Online: 29 April 2024

Declarations

:

: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

: Not applicable.

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