Malicious encrypted traffic features extraction model based on unsupervised feature adaptive learning-Reference-Cited by-同舟云学术

Malicious encrypted traffic features extraction model based on unsupervised feature adaptive learning

Published:2022-05-22 Issue:4 Volume:18 Page:453-463
ISSN:2263-8733
Container-title:Journal of Computer Virology and Hacking Techniques
language:en
Short-container-title:J Comput Virol Hack Tech

Author:

Zhou Zhihong,Bin Hu,Li Jianhua,Yin Ying,Chen Xiuzhen,Ma Jin,Yao Lihong

Funder

National Key Research and Development Program of China

Shanghai industrial foundation project

Publisher

Springer Science and Business Media LLC

Subject

Computational Theory and Mathematics,Hardware and Architecture,Software,Computer Science (miscellaneous)

Link

https://link.springer.com/content/pdf/10.1007/s11416-022-00429-y.pdf

Reference26 articles.

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3. Zhao, B., Guo, H., Liu, Q.R., et al.: Protocol independent identification of encrypted traffic based on weighted eumnlative sum test. J. Softw. 24(6), 1334–1345 (2013)

4. Velan, P., Cermak, M., Celeda, P., et al.: A survey of methods for encrypted traffic classification and analysis. Int. J. Netw. Manag. 25(5), 355–374 (2015)

5. Khakpour, A.R., Liu, A.X.: An information-theoretical approach to high-speed flow nature identification. IEEE/ACM Trans. Netw. (TON) 21(4), 1076–1089 (2013)

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