基于深度卷积神经网络的网络流量分类方法

王勇; 周慧怡; 俸皓; 叶苗; 柯文龙

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

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基于深度卷积神经网络的网络流量分类方法

学术论文 | 更新时间：2024-06-05

- 基于深度卷积神经网络的网络流量分类方法
- Network traffic classification method basing on CNN
- 通信学报 2018年39卷第1期页码：14-23
- 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：
  
  [ "高博" ]
- 基金信息：
  
  国家自然科学基金资助项目(61662018);国家自然科学基金资助项目(61661015);中国博士后科学基金资助项目(2016M602922XB);广西省自然科学基金资助项目(2016GXNSFAA380153);桂林电子科技大学研究生教育创新计划基金资助项目(2018YJCX53);桂林电子科技大学研究生教育创新计划基金资助项目(2018YJCX20);桂林理工大学科研启动基金资助项目(GUTQDJJ20172000019)
- DOI：10.11959/j.issn.1000-436x.2018018
  中图分类号： TP393
- 网络出版日期：2018-01，
  
  纸质出版日期：2018-01-25
- 稿件说明：
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王勇, 周慧怡, 俸皓, 等. 基于深度卷积神经网络的网络流量分类方法[J]. 通信学报, 2018,39(1):14-23.

Yong WANG, Huiyi ZHOU, Hao FENG, et al. Network traffic classification method basing on CNN[J]. Journal on communications, 2018, 39(1): 14-23.
王勇, 周慧怡, 俸皓, 等. 基于深度卷积神经网络的网络流量分类方法[J]. 通信学报, 2018,39(1):14-23. DOI： 10.11959/j.issn.1000-436x.2018018.

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.

关键词

Keywords

references

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