A Method of Detecting the Abnormal Encrypted Traffic Based on Machine Learning and Behavior Characteristics-Reference-Cited by-同舟云学术

A Method of Detecting the Abnormal Encrypted Traffic Based on Machine Learning and Behavior Characteristics

Published:2019-11-15 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2019 the 9th International Conference on Communication and Network Security
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Author:

Kong Bin¹,Liu Zhangpu²,Zhou Guangming³,Yu Xiaoyan²

Affiliation:

1. School of Economics and Management, Beijing Jiaotong University

2. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China, School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

3. China National Salt Industry Group Co., Ltd, Beijing, China

Publisher

ACM

Reference12 articles.

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3. Proc. of the INFOCOM IEEE Conf. on Computer Communications Workshops. San Diego: IEEE Press Proc. of the INFOCOM IEEE Conf. on Computer Communications Workshops. San Diego: IEEE Press G. Aceto A. Dainotti W. Donato A Pescapé 2010. PortLoad: Taking the best of two worlds in traffic classification 2010

4. Guo ZB. and Qiu ZD. 2008. Identification of BitTorrent traffic for high speed network using packet sampling and application signatures.Journal of Computer Research and Development 45(2):227--236. Guo ZB. and Qiu ZD. 2008. Identification of BitTorrent traffic for high speed network using packet sampling and application signatures.Journal of Computer Research and Development 45(2):227--236.

5. Proc. of the ACM SIGCOMM 2008 Conf. on Data Communication (SIGCOMM New York Proc. of the ACM SIGCOMM 2008 Conf. on Data Communication (SIGCOMM R. Smith C. Estan S. Jha SJ Kong 2008. Deflating the big bang: Fast and scalable deep packet inspection with extended finite automata.In: Bahl V Wetherall D Savage S Stoica I eds 2008

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