基于深度卷积神经网络的网络流量分类方法

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.%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.