Art
J-GLOBAL ID:202102251861734355
Reference number:21A1370758
Federated Learning-Based Network Intrusion Detection with a Feature Selection Approach
特徴量選択アプローチと連合学習によるネットワーク侵入検知手法の検討
Author (2):
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Material:
Volume:
2021
Issue:
ARC-244
Page:
Vol.2021-ARC-244,No.24,1-7 (WEB ONLY)
Publication year:
Mar. 18, 2021
JST Material Number:
U0451A
Document type:
Proceedings
Article type:
原著論文
Country of issue:
Japan (JPN)
Language:
JAPANESE (JA)
Thesaurus term:
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Semi thesaurus term:
Thesaurus term/Semi thesaurus term
Keywords indexed to the article.
All keywords is available on JDreamIII(charged).
On J-GLOBAL, this item will be available after more than half a year after the record posted. In addtion, medical articles require to login to MyJ-GLOBAL.
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JST classification (2):
JST classification
Category name(code) classified by JST.
Data protection
, Computer networks
Reference (15):
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Drewek-Ossowicka, A., Pietrołaj, M., & Rumiński, J. (2020). A survey of neural networks usage for intrusion detection systems. Journal of Ambient Intelligence and Humanized Computing, 1-18.
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Gamage, S., & Samarabandu, J. (2020). Deep learning methods in network intrusion detection: A survey and an objective comparison. Journal of Network and Computer Applications, 169, 102767.
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Ahmad, Z., Shahid Khan, A., Wai Shiang, C., Abdullah, J., & Ahmad, F. (2021). Network intrusion detection system: A systematic study of machine learning and deep learning approaches. Transactions on Emerging Telecommunications Technologies, 32(1), e4150.
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Tsukada, M., Kondo, M., & Matsutani, H. (2020). A neural network-based on-device learning anomaly detector for edge devices. IEEE Transactions on Computers, 69(7), 1027-1044.
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Stiawan, D., Idris, M. Y. B., Bamhdi, A. M., & Budiarto, R. (2020). CICIDS-2017 dataset feature analysis with information gain for anomaly detection. IEEE Access, 8, 132911-132921.
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Terms in the title
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