Network traffic classification method basing on CNN

Yong WANG; Huiyi ZHOU; Hao FENG; Miao YE; Wenlong KE

doi:10.11959/j.issn.1000-436x.2018018

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Network traffic classification method basing on CNN

Papers | 更新时间：2024-06-05

- Network traffic classification method basing on CNN
- Journal on Communications Vol. 39, Issue 1, Pages: 14-23(2018)
- 作者机构：
  
  1. 桂林电子科技大学计算机与信息安全学院，广西桂林 541004
  2. 桂林电子科技大学信息与通信学院，广西桂林 541004
  3. 桂林电子科技大学认知无线电与信息处理省部共建教育部重点实验室，广西桂林 541004
  4. 桂林理工大学信息科学与工程学院，广西桂林 541004
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61662018);The National Natural Science Foundation of China(61661015);Project Funded by China Postdoctoral Science Foundation(2016M602922XB);The Natural Science Foundation of Guangxi(2016GXNSFAA380153);Innovation Project of Guest Graduate Education(2018YJCX53);Innovation Project of Guest Graduate Education(2018YJCX20);Foundation of Guilin University of Technology(GUTQDJJ20172000019)
- DOI：10.11959/j.issn.1000-436x.2018018
  CLC： TP393
- Online First：2018-01，
  
  Published：25 January 2018
- 稿件说明：
移动端阅览
Yong WANG, Huiyi ZHOU, Hao FENG, et al. Network traffic classification method basing on CNN[J]. Journal on Communications, 2018, 39(1): 14-23.
DOI：

Yong WANG, Huiyi ZHOU, Hao FENG, et al. Network traffic classification method basing on CNN[J]. Journal on Communications, 2018, 39(1): 14-23. DOI： 10.11959/j.issn.1000-436x.2018018.

摘要

针对传统基于机器学习的流量分类方法中特征选取环节的好坏会直接影响结果精度的问题，提出一种基于卷积神经网络的流量分类算法。首先，通过对数据进行归一化处理后映射成灰度图片作为卷积神经网络的输入数据，然后，基于LeNet-5深度卷积神经网络设计适于流量分类应用的卷积层特征面及全连接层的参数，构造能够实现流量的自主特征学习的最优分类模型，从而实现网络流量的分类。所提方法可以在避免复杂显式特征提取的同时达到提高分类精度的效果。通过公开数据集和实际数据集的系列仿真实验测试结果表明，与传统分类方法相比所提算法基于改进的CNN流量分类方法不仅提高了流量分类的精度，而且减少了分类所用的时间。

Abstract

Since the feature selection process will directly affect the accuracy of the traffic classification based on the traditional machine learning method

a traffic classification algorithm based on convolution neural network was tailored.First

the min-max normalization method was utilized to process the traffic data and map them into gray images

which would be used as the input data of convolution neural network to realize the independent feature learning.Then

an improved structure of the classical convolution neural network was proposed

and the parameters of the feature map and the full connection layer were designed to select the optimal classification model to realize the traffic classification.The tailored method can improve the classification accuracy without the complex operation of the network traffic.A series of simulation test results with the public data sets and real data sets show that compared with the traditional classification methods

the tailored convolution neural network traffic classification method can improve the accuracy and reduce the time of classification.

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Keywords

references

LENCUN Y , BENGIO Y , HINTON G . Deep learning [J ] . Nature , 2015 , 512 : 436 - 444 .

DING J , HUANG L , YUPNEG T , et al . A flow nature classification method based on multi-features of n-gram [J ] . Computer Applications＆ Software , 2017 .

GUANG L , RONGHUA G . Cascaded classifier for improving traffic classification accuracy [J ] . IET Communications , 2017 , 11 : 1751 - 1758 .

SCHMIDT B , AL-FUQAHA A , GUPTA A . Optimizing an artificial immune system algorithm in support of flow-based internet traffic classification [J ] . Applied Soft Computing , 2017 , 54 : 1 - 22 .

DONG Y N , ZHAO J J , JIM J . Novel feature selection and classification of Internet video traffic based on a hierarchical scheme [J ] . Computer networks , 2017 , 119 : 102 - 111 .

KORNCKY J , ABDUL-HAMEED O , KONDOZ A . Radio frequency traffic classification over WLAN [J ] . IEEE-ACM Transactions on Networking , 2017 , 25 : 56 - 68 .

TONG D , QU Y R , PRASANNA V K . Accelerating decision tree based traffic classification on FPGA and multicore platforms [J ] . IEEE Transactions on Parallel ＆ Distributed Systems , 2017 :1.

Shi H , Li H , Zhang D , et al . Efficient and robust feature extraction and selection for traffic classification [J ] . Computer Networks the International Journal of Computer ＆ Telecommunications Networking , 2017 , 119 ( C ): 1 - 16 .

CAO J , FANG Z Y , QU G N , et al . An accurate traffic classification model based on support vector machines [J ] .,2017,27. Networks , 2017 , 27 .

HE K M , ZHANG X Y , REN S Q . Delving deep into rectifiers:surpassing human-level performance on imagenet classification [C ] // IEEE International Conference on Computer Vision . 2015 : 1026 - 1034 .

SUN S N , ZHANG B B , XIE L , et al . An unsupervised deep domain adaptation approach for robust speech recognition [J ] . Neurocomputing , 2017 , 257 : 79 - 87 .

WILLIAMSON D S , WANG D L . Time-frequency masking in the complex domain for speech dereverberation and denoising [J ] . IEEE/ACM Transactions on Audio Speech ＆ Language Processing , 2017 , 25 : 1492 - 1501 .

ZHANG Y , MARSHALL I , WALLACE B C . Rationale-augmented convolutional neural networks for text classification [C ] // Conf Empir Methods Nat Lang Process . 2016 :795.

ERTAM F , AVCI E . A new approach for internet traffic classification:GA-WK-ELM [J ] . Measurement , 2017 , 95 : 135 - 142 .

白雪 . 基于DBN 的网络流量分类的研究 [D ] . 呼和浩特:内蒙古大学 , 2015 .

BAI X . Research on Internet traffic classification using DBN [D ] . Hohehot:Inner Mongolia University , 2015 .

MOORE A W , ZUEV D . Discriminators for use in flow-based classification [R ] . Technical report,Intel Research,Cambridge , 2005 .

周飞燕 , 金林鹏 , 董军 . 卷积神经网络研究综述 [J ] . 计算机学报 , 2017 , 40 ( 6 ): 1 - 23 .

ZHOU F Y , JIN L P , DONG J . Review of convolutional neural network [J ] . Chinese Journal of Computers , 2017 , 40 ( 6 ): 1 - 23 .

LENCUN Y , BOTTOU L , BENGIO Y . Gradient-based learning applied to document recognition [J ] . Proceedings of the IEEE , 1998 , 862 : 2278 - 2324 .

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 .

寇广 , 汤光明 , 王硕 , 等 . 深度学习在僵尸云检测中的应用研究 [J ] . 通信学报 , 2016 , 37 ( 11 ): 114 - 128 .

KOU G , TANG G M , WANG S , et al . Using deep learning for detecting BotCloud [J ] . Journal on Communications , 2016 , 37 ( 11 ): 114 - 128 .

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