通信学报2018，Vol.39Issue(1)：14-23,10.DOI:10.11959/j.issn.1000-436x.2018018
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
摘要
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
流量分类/卷积神经网络/归一化/特征选择Key words
network traffic classification/convolutional neural network/normalized/feature selection分类
信息技术与安全科学引用本文复制引用
王勇,周慧怡,俸皓,叶苗,柯文龙..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):14-23,10.基金项目
国家自然科学基金资助项目(No.61662018,No.61661015) （No.61662018,No.61661015）
中国博士后科学基金资助项目(No.2016M602922XB) （No.2016M602922XB）
广西自然科学基金资助项目(No.2016GXNSFAA380153) （No.2016GXNSFAA380153）
桂林电子科技大学研究生教育创新计划基金资助项目(No.2018YJCX53,No.2018YJCX20) （No.2018YJCX53,No.2018YJCX20）
桂林理工大学科研启动基金资助项目(No.GUTQDJJ20172000019)The National Natural Science Foundation of China (No.61662018,No.61661015),Project Funded by China Postdoctoral Foundation (No.2016M602922XB),The Natural Science Foundation of Guangxi Autonomous Region (No.2016GXNSFAA380153),Innovation Project of Guest Graduate Education (No.2018YJCX53,No.2018YJCX20),Foundation of Guilin University of Technology (No.GUTQDJJ20172000019) （No.GUTQDJJ20172000019）
