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J-GLOBAL ID:202202267222071075   Reference number:22A1037478

Intrusion Detection System on IoT with 5G Network Using Deep Learning

深層学習を用いた5Gネットワークを用いたIoTにおける侵入検知システム【JST・京大機械翻訳】
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Volume: 2022  Page: Null  Publication year: 2022 
JST Material Number: W1338A  ISSN: 1530-8669  Document type: Article
Article type: 原著論文  Country of issue: United Kingdom (GBR)  Language: ENGLISH (EN)
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The Internet of Things (IoT) c...
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Mobile communication  ,  Data protection 
Reference (33):
  • B. Susilo, R. F. Sari, "Intrusion detection in IoT networks using deep learning algorithm," Information, vol. 11, no. 5, pp. 279, 2020.
  • A. Aldweesh, A. Derhab, A. Z. Emam, "Deep learning approaches for anomaly-based intrusion detection systems: a survey, taxonomy, and open issues," Knowledge-Based Systems, vol. 189, pp. 105124, 2020.
  • L. Fernández Maimó, A. Huertas Celdrán, M. Gil Pérez, F. J. García Clemente, G. Martínez Pérez, "Dynamic management of a deep learning-based anomaly detection system for 5G networks," Journal of Ambient Intelligence and Humanized Computing, vol. 10, no. 8, pp. 3083-3097, 2019.
  • V. Yihua Liao, "Rao Vemuri, Use of K-nearest neighbor classifier for intrusion detection11An earlier version of this paper is to appear in the Proceedings of the 11th USENIX Security Symposium, San Francisco, CA, August 2002," Computers & Security, vol. 21, no. 5, pp. 439-448, 2002.
  • N. Abdullah, Advances in Cyber Security: Third International Conference, ACeS 2021, Penang, Malaysia, August 24-25, 2021, Revised Selected Papers, Springer Nature, Penang, Malaysia, 2021, https://link.springer.com/book/10.1007/978-981-16-8059-5.
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