A convolutional neural network intrusion detection method based on data imbalance-Reference-Cited by-同舟云学术

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A convolutional neural network intrusion detection method based on data imbalance

Published:2022-06-21 Issue:18 Volume:78 Page:19401-19434
ISSN:0920-8542
Container-title:The Journal of Supercomputing
language:en
Short-container-title:J Supercomput

Author:

Gan Baiqiang^ORCID,Chen Yuqiang,Dong Qiuping,Guo Jianlan,Wang Rongxia

Funder

scientific research project of special projects in key areas of Guangdong Provincial Department of Education

Guangdong Provincial Department of Education

Guangzhou philosophy and social science project

Key scientific research projects of Guangzhou Nanyang Polytechnic

Key projects of social science and technology development in Dongguan under Grant

Special fund for Dongguan's Rural Revitalization Strategy in 2021

Dongguan special commissioner project

Guangdong-Dongguan Joint fund for Basic and Applied Research of Guangdong Province

Publisher

Springer Science and Business Media LLC

Subject

Hardware and Architecture,Information Systems,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s11227-022-04633-x.pdf

Reference46 articles.

1. Xia LM, Li ZM (2021) A new method of abnormal behavior detection using LSTM network with temporal attention mechanism. J Supercomput 77(4):773223–773241. https://doi.org/10.1007/s11227-020-03391-y

2. Zhu YK, Gaba GS, Almansour FM, Alroobaea R, Masud M (2021) Application of data mining technology in detecting network intrusion and security maintenance. J Intell Syst 30(1):664–676. https://doi.org/10.1515/jisys-2020-0146

3. Lu HM, Wang T, Xu X, Wang T (2022) Cognitive memory-guided autoencoder for effective intrusion detection in internet of things. IEEE Trans Industr Inf 18(5):3358–3366. https://doi.org/10.1109/TII.2021.3102637

4. Ramalingam GP, Annie RAX, Gopalakrishnan S (2022) Optimized fuzzy enabled semi-supervised intrusion detection system for attack prediction. Intell Autom Soft Comput 32(3):1479–1492. https://doi.org/10.32604/iasc.2022.022211

5. Guo YQ, Wang X (2021) Applying TS-DBN model into sports behavior recognition with deep learning approach. J Supercomput 77(10):12192–12208. https://doi.org/10.1007/s11227-021-03772-x

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