Anti-Attack Intrusion Detection Model Based on MPNN and Traffic Spatiotemporal Characteristics-Reference-Cited by-同舟云学术

Anti-Attack Intrusion Detection Model Based on MPNN and Traffic Spatiotemporal Characteristics

Published:2023-10-28 Issue:4 Volume:21 Page:
ISSN:1570-7873
Container-title:Journal of Grid Computing
language:en
Short-container-title:J Grid Computing

Author:

Lu Jiazhong,Lan Jin,Huang Yuanyuan,Song Maojia,Liu Xiaolei

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Information Systems,Software

Link

https://link.springer.com/content/pdf/10.1007/s10723-023-09703-9.pdf

Reference37 articles.

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2. Gao Y, Chen J, Miao H, et al. 2022. Self-Learning Spatial Distribution-Based Intrusion Detection for Industrial Cyber-Physical Systems. IEEE Transactions on Computational Social Systems. IEEE, 1-10. https://doi.org/10.1109/TCSS.2021.3135586

3. Mubarak, S., Habaebi, M.H., Islam, M.R., et al.: Industrial Datasets with ICS Testbed and Attack Detection Using Machine Learning Techniques. Intelligent Automation And Soft Computing. Tech Sci Press. 31(3), 1345–1360 (2022). https://doi.org/10.32604/iasc.2022.020801

4. Such, F.P., Sah, S., Dominguez, M.A., et al.: Robust spatial filtering with graph convolutional neural networks. IEEE J Select Top Sign Process. IEEE. 11(6), 884–896. (2017). https://doi.org/10.1109/JSTSP.2017.2726981

5. Kapoor A, Ben X, Liu L, et al. 2020. Examining covid-19 forecasting using spatio-temporal graph neural networks. arXiv:2007.03113. Retrieved from https://arxiv.org/abs/2007.03113

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