Internet traffic classification method based on behavior feature learning

Zhen LIU; Ruoyu WANG

doi:10.11959/j.issn.1000-0801.2016152

您当前的位置：

首页 >

文章列表页 >

Internet traffic classification method based on behavior feature learning

research and development | 更新时间：2024-06-05

- Internet traffic classification method based on behavior feature learning
- Telecommunications Science Vol. 32, Issue 6, Pages: 143-152(2016)
- 作者机构：
  
  1. 广东药科大学医药信息工程学院，广东广州 510006
  2. 华南理工大学信息网络工程研究中心，广东广州 510006
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2016152
  CLC： TP393
- Published Online：2016-06，
  
  Published：20 June 2016
- 稿件说明：
移动端阅览
Zhen LIU, Ruoyu WANG. Internet traffic classification method based on behavior feature learning[J]. Telecommunications science, 2016, 32(6): 143-152.
DOI：

Zhen LIU, Ruoyu WANG. Internet traffic classification method based on behavior feature learning[J]. Telecommunications science, 2016, 32(6): 143-152. DOI： 10.11959/j.issn.1000-0801.2016152.

摘要

基于连接图的互联网流量分类方法能反映主机间的通信行为，具有较高的分类稳定性，但是经验式总结的启发式规则有限，难以获得高分类准确率。研究分析了主机间通信行为模式和BOF方法，从具有相同｛目的IP 地址，目的端口号，传输层协议｝网络流量中，提取主机间连接相关的行为统计特征（HCBF），采用C4.5决策树算法学习基于行为特征的分类规则，其无需人工建立启发式规则。在传统互联网和移动互联网流量数据集上，从基本分类性能和分类稳定性方面，与现有的特征集进行比较分析，实验结果表明，HCBF 特征集合的类间区分能力和稳定性较高。

Abstract

The connection graph based internet traffic classification method can reflect the connectivity behavior between hosts.Thus，it has high stability.But the heuristic rules summarized for traffic classification are generally incomplete，and they difficultly obtain high classification accuracy.Host communication behavior model and BOF method was researched，and a set of host connection related behavior features （HCBF）was extracted from the multiple flows with the same ｛destination IP，destination port and transport protocol｝.To evaluate the performance of HCBF，it was compared with the existing feature set on the respect of basic classification performance and classification stability.The experiments were carried out on the traffic collected in the traditional and mobile networks.Results show that HCBF out performs existing feature sets.

关键词

Keywords

references

WANG Y ， XIANG Y ， ZHANG J ， et al . Internet traffic clustering with side information [J ] . Journal of Computer and System Sciences ， 2014 ， 80 （ 5 ）： 1021 - 1036 .

ZHANG J ， CHEN X ， XIANG Y ， et al . Robust Network Traffic Classification [J ] . IEEE/ACM Transactions on Networking ， 2015 ， 23 （ 4 ）： 1257 - 1270 .

DAINOTTI A ， PESCAP A . Issues and future directions in traffic classification [J ] . IEEE Network ， 2012 ， 26 （ 1 ）： 35 - 40 .

CALLADO A ， KAMIENSKI C . A survey on Internet traffic identification [J ] . IEEE Communications Surveys ＆ Tutorials ， 2009 ， 11 （ 3 ）： 37 - 52 .

KARAGIANNIS T ， PAPAGIANNAKI K ， FALOUTSOS M . BLINC：multilevel traffic classification in the dark [J ] . ACM SIGCOMM ， 2005 ， 35 （ 4 ）： 229 - 240 .

ILIOFOTOU M ， KIM H ， FALOUTSOS M ， et al . Graption：a graph-based P2P traffic classification framework for the internet backbone [J ] . Computer Networks ， 2011 ， 55 （ 8 ）： 1909 - 1920 .

BERMOLEN P ， MELLIA M ， MEOB M ， et al . Abacus：accurate behavioral classification of P2P-TV traffic [J ] . Computer Networks ， 2011 （ 55 ）： 1394 - 1411 .

ASAI H ， FUKUDA K ， ESAKI H . Traffic causality graphs：profiling network applications through temporal and spatial causality of flows [C ] // The 23rd International Teletraffic Congress，Sept 6-9，2011，San Francisco，CA，USA . New Jersey ： IEEE Press ， 2011 ： 95 - 102 .

MOORE A ， ZUEV D ， CROGAN M . Discriminators for use in flow-based classification [C ] // In Passive ＆ Measurement Workshop 2003（PAM2005），August1，2005，London，England.[S.1.：s.n.] . 2005 .

HAJJAR A ， KHALIFE J，DAZ-VERDEJO J . Network traffic application identification based on message size analysis [J ] . Journal of Network and Computer Applications ， 2015 （ 58 ）： 130 - 143 .

LIU Z ， WANG R Y ， TAO M ， et al . A class-oriented feature selection approach for multi-class imbalanced traffic datasets based on local and global metrics fusion [J ] . Neurocomputing ， 2015 （ 168 ）： 365 - 381 .

FAHAD A ， TARI Z ， KHALIL I ， et al . Toward an efficient and scalable feature selection approach for internet traffic classification [J ] . Computer Networks ， 2013 ， 57 （ 9 ）： 2040 - 2057 .

HONG Y ， HUANG C C ， NANDY B ， et al . Iterative-tuning support vector machine for network traffic classification [C ] // The 2015 IFIP/IEEE International Symposium on Integrated Network Management，May 11-15，2015，Ottawa，ON，Canada . New Jersey ： IEEE Press ， 2015 ： 458 - 466 .

HJELMVIK E ， JOHN W . Breaking and improving protocol obfuscation：No.2010-05，ISSN 1652-926X [R ] .[S.1.：s.n. ] ， 2010 ： 1 - 34 .

LEE S ， KIM H ， BARMAN D ， et al . NeTraMark：a network traffic classification benchmark [J ] . ACM SIGCOMM Computer Communication Review ， 2011 ， 41 （ 1 ）： 23 - 30 .

GRINGOLI F ， SALGARELLI L ， DUSI M ， et al . GT：picking up the truth from the ground for internet traffic [J ] . ACM SIGCOMM Computer Communication Review ， 2009 ， 39 （ 5 ）： 13 - 18 .

ZHANG J ， XIANG Y ， WANG Y ， et al . Network traffic classification using correlation information [J ] . IEEE Transactions on Parallel ＆ Distributed Systems ， 2013 ， 24 （ 1 ）： 104 - 117 .

WILLIAMS N ， ZANDER S ， ARMITAGE G . A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification [J ] . SIGCOMM Computer Communication Review ， 2006 ， 30 （ 5 ）： 5 - 16 .

徐鹏，林森 . 基于C4.5决策树的流量分类方法 [J ] . 软件学报， 2009 ， 20 （ 10 ）： 2692 - 2704 .

XU P ， LIN S.Internet traffic classification using C4 . 5 decision tree [J ] . Journal of Software ， 2009 ， 20 （ 10 ）： 2692 - 2704 .

Views

901

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research on telecom industry customer churn prediction based on explainable machine learning models

A single flow detection enabled method for DDoS attacks in IoT based on traffic feature reconstruction and mapping

Research and application of adaptive algorithm for 5G voice quality evaluation

A spatial diversity cooperative spectrum sensing system based on blockchain and deep learning

Gesture recognition based on flexible solar cells and ultrathin hydrogel film

Related Author

WANG Shengjie

ZHANG Qinghong

Lixia XIE

Bingdi YUAN

Hongyu YANG

Ze HU

Xiang CHENG

Liang ZHANG

Related Institution

College of Statistics and Data Science, Xinjiang University of Finance and Economics

School of Safety Science and Engineering, Civil Aviation University of China

School of Information, The University of Arizona

School of Information Engineering, Yangzhou University

School of Computer Science and Technology, Civil Aviation University of China

⁰