基于C4.5决策树的HTTPS加密流量分类方法

doi:10.11896/JsJkx.191200155

计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 381-385.doi: 10.11896/JsJkx.191200155

基于C4.5决策树的HTTPS加密流量分类方法

邹洁¹, 朱国胜¹, 祁小云², 曹扬晨¹

1 湖北大学计算机与信息工程学院武汉430062;
2 湖北大学化学化工学院武汉 430062

发布日期:2020-07-07
通讯作者: 朱国胜(zhuguosheng@hubu.edu.cn)
作者简介:292370368@qq.com
基金资助:
赛尔网络下一代互联网技术创新项目(NGII20180411)

HTTPS Encrypted Traffic Classification Method Based on C4.5 Decision Tree

ZOU Jie¹, ZHU Guo-sheng¹, QI Xiao-yun² and CAO Yang-chen¹

1 School of Computer and Information Engineering,Hubei University,Wuhan 430062,China
2 School of Chemistry and Chemical Engineering,Hubei University,Wuhan 430062,China

Published:2020-07-07
About author:ZOU Jie, born in 1996, postgraduate.Her main research interests include machine learning and network traffic analysis.
ZHU Guo-sheng, born in 1972, Ph.D, professor.His main research interests include next-generation Internet and software-defined networks.
Supported by:
This work was supported by CERNET Innovation ProJect (NGII20180411).

摘要/Abstract

摘要： HTTPS协议基于原本不具有加密机制的HTTP协议。将其与SSL/TLS协议组合,在传输数据之前,客户端与服务器端之间进行一次SSL/TLS 握手,并协商通信过程中使用的加密套件,以安全地交换密钥并且实现双方的身份验证,建立安全通信线路后,对 HTTP 应用协议数据进行加密传输,防止通信内容被窃听和篡改。传统的基于有效载荷的方法已无法处理加密流量,基于流量特征和机器学习的加密流量分类和分析成为目前的主流方法,其通过建立监督学习模型,在保证加密完整性的条件下,基于网络流数据特征工程,应用C4.5决策树算法,在局域网环境中对腾讯网中应用HTTPS加密数据传输流进行分析,可有效实现对该网站HTTPS加密流量进行模块内容的精确分类。

关键词: HTTPS, SSL/TLS, 分类, 加密流量, 决策树

Abstract: The HTTPS protocol is based on the HTTP protocol that does not have an encryption mechanism.By combining with the SSL/TLS protocol,an SSL/TLS handshake is performed between the client and the server before the data is transmitted,and the cipher suite used in the communication process is negotiated to securely exchange secret keys and implement mutual authentication.After establishing a secure communication line,the HTTP application protocol data is encrypted and transmitted,preventing the risk of eavesdropping and tampering of the communication content.The traditional payload-based method can’t handle encrypted traffic.The classification and analysis of encrypted traffic based on traffic characteristics and machine learning have become the mainstream method.By establishing a supervised learning model,based on network flow data feature engineering,under the condition of ensuring encryption integrity,the C4.5 decision tree algorithm is applied in the LAN environment to analyze the application of HTTPS encrypted data transmission stream in Tencent network,which can effectively realize accurate classification of the website HTTPS encrypted traffic.

Key words: Classification, Decision tree, Encrypted traffic, HTTPS, SSL/TLS

中图分类号:

TP181

邹洁, 朱国胜, 祁小云, 曹扬晨. 基于C4.5决策树的HTTPS加密流量分类方法[J]. 计算机科学, 2020, 47(6A): 381-385. https://doi.org/10.11896/JsJkx.191200155

ZOU Jie, ZHU Guo-sheng, QI Xiao-yun and CAO Yang-chen. HTTPS Encrypted Traffic Classification Method Based on C4.5 Decision Tree[J]. Computer Science, 2020, 47(6A): 381-385. https://doi.org/10.11896/JsJkx.191200155

参考文献

[1] HOLZ R,BRAUN L,KAMMENHUBER N,et al.The SSL Landscape:A Thorough Analysis of the X.509 PKI Using Active and Passive Measurements//Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference(IMC ’11).New York,NY,USA,ACM,2011:427-444.
[2] SUN G,XUE Y,DONG Y,et al.An Novel Hybrid Method for Effectively Classifying Encrypted Traffic//Global Telecommunications Conference (GLOBECOM 2010).IEEE,2010:1-5.
[3] ARNDT D J,ZINCIR-HEYWOOD A N.A Comparison of Three Machine Learning Techniques for Encrypted Network Traffic Analysis//IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA).2011:107-114.
[4] MILLER B,HUANG L,JOSEPH A D,et al.Tygar.I Know Why You Went to the Clinic:Risks and Realization of HTTPS Traffic Analysis//Privacy Enhancing Technologies,volume 8555 of Lecture Notes in Computer Science.Springer International Publishing,2014:143-163.
[5] KORCZYNSKI M A.Classifying Service Flows in the Encrypted Skype Traffic//2012 IEEE International Conference on Communications (ICC).2012:1064-1068.
[6] WANG T,CAI X,NITHYANAND R,et al.Effective attacks and provable defenses for website fingerprinting//23rd {USENIX} Security Symposium ({USENIX}.2014:143-157.
[7] CHENG G,CHEN Y X.Encrypted Traffic Identification MethodBased on Support Vector Machine.Journal of Southeast University(Natural Science Edition),2017(4):655-659.
[8] CHEN W,HU L,YANG L.Fast Identification Method of Encrypted Traffic Based on Load Characteristics.Computer Engineering.2012(12):22-25.
[9] ZHANG B Y.Analysis of the Principle and Application of HTTPS Protocol.Network Security Technology and Application,2016(7):36-37.
[10] XU P,LIN S.Traffic Classification Method Based on C4.5 Decision Tree .Journal of Software,2009(10):2692-2704.
[11] LIU K.Research on feature selection in network flow classification .Yangzhou:Yangzhou University,2013:18-19.
[12] ZHOU Z H.Machine Learning .BeiJing:Tsinghua University Press,2016:73-79.

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基于C4.5决策树的HTTPS加密流量分类方法

HTTPS Encrypted Traffic Classification Method Based on C4.5 Decision Tree

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