一、网络流量分类
图1 加密流量识别研究进展
二、异常加密流量分类
三、总结与展望
参考文献
[1]陈良臣,高曙,刘宝旭等.网络加密流量识别研究进展及发展趋势[J].信息网络安全, 2019.19(3):19-25.
[2]藩吴斌,程光,郭晓军,黄顺翔. 网络加密流量识别研究综述及展望[J].通信学报, 2016,37(9):154-167.
[3] MA Ruolong. Research and Implementation of Unknown and Encrypted Traffic Identification Based on Convolutional Neural Network[D]. Beijing: Beijing University of Posts and Telecommunications,2018.
[4]李光松,李文清,李青. 基于随机性特征的加密和压缩流量分类[J],吉林大学学报(工学版),2020.09.
[5] Alshammari R, Zincir-Heywood A N. Machine Learning-based Encrypted Traffic Classification: Identifying SSH and Skype[C]//IEEE.2019 IEEE Symposium on Computational Intelligence for Security and Defense Applications, July 8-10,2009, Ottawa, ON, Canada. NJ: IEEE.2009:1-8.
[6] Wright C V, Monrose F, Masson G M. Using Visual Motifs to Classify Encrypted Traffic[C]//ACM. The 3rd International Workshop on Visualization for Computer Security, November 3, 2006, Alexandria, Virginia, USA. New York: ACM, 2006:41- 50.
[7] Gao Changxi, Wu Yabiao, Wang Cong. Encrypted Traffic Classification Based on Packet Length Distribution of Sampling Sequence[J]. Journal on Communications, 2015.36 (9):65-75.
[8]孙中军,翟江涛, 戴跃伟. 一种基于DPI和负载随机性的加密流量识别方法[J].应用科学学报,2019.9(05)
[9]王伟.基于深度学习的网络流量分类及异常检测方法研究[D].中国科学技术大学,2018.
[10] Yanmiao Li, Hao Guo, Jiangang Hou, et al. A Survey of Encrypted Malicious Traffic Detection. 2021 International Conference on Communications, Cybersecurity, and Informatics (CCCI), 2021. 9583191.
[11] Chen Tieming, Jin Chengqiang, Liu Mingqi, Zhu Tiantian. Intelligent detection method on network malicious traffic based on sample enhancement[J]. Journal on Communications, 2020,41(06):128-138
[12] Hwang R, Peng M, Huang C, et al. An unsupervised deep learning model for early network traffic anomaly detection[J]. IEEE Access,2020,8:30387-30399.
[13] Dai Rui, Gao Chuan, Lang Bo. SSL Malicious Traffic Detection Based On Multi-view Features. Proceedings of the 2019 the 9th International Conference on Communication and Network Security[C]. New York, NY, USA:ACM,2019.40-46.
原文始发于微信公众号(中国保密协会科学技术分会):移动加密流量分类识别研究概述
- 左青龙
- 微信扫一扫
-
- 右白虎
- 微信扫一扫
-
评论