一种基于残差网络的改进网络流量分类算法

基于卷积神经网络的网络流量分类算法中,为了提高分类准确度,其结构设计日趋复杂,容易出现梯度下滑甚至梯度消失,导致预测准确度不升反降。文章提出了一种基于残差网络的改进流量分类算法,引入残差网络层代替传统卷积神经网络中的卷积层和池化层,不仅缓解了传统卷积网络因层次太深导致难以训练的问题,同时与传统卷积运算相比,所提出的残差网络在训练时学习到的数据特征信息更加全面,训练后的模型也更加准确。仿真结果表明,改进后的算法比常规的神经网络算法表现更佳,分类准确度从92.05%提高到了96.18%。

A Residual Network based Improved Network Traffic Classification Algorithm

Convolutional neural network based network traffic classification scheme suffers many disadvantages such as the complex structure designing,gradient declines or even explodes,the deterioration of prediction accuracy,and etc.A residual network based improved traffic classification algorithm is proposed.The convolution layer and pooling layer in the traditional convolutional neural network are replaced by the residual network layer,which can alleviate the problem that the traditional convolution network is too deep to train effectively.The data feature information learned by the proposed algorithm in the training stage is more comprehensive,and the trained model can also be more accurate.Simulation results show that the improved algorithm has higher accuracy than the traditional neural network,which can be improved from 92.05%to 96.18%.