Large-scale network burst traffic identification model and method and training method of model

Abstract

The invention provides a large-scale network burst traffic identification model, a large-scale network burst traffic identification method and a training method of the model. The model is built on Spark through a TensorFlowOnSpark framework; the model comprises an input layer, a first convolution layer, a first maximum pooling layer, a second convolution layer, a second maximum pooling layer, a third convolution layer, a fourth convolution layer, a fifth convolution layer, a third maximum pooling layer, a full connection layer and an output layer which are connected in sequence, the input layer receives a 32 * 32 data input form; the first convolution layer adopts 96 pieces of 5 * 5 convolution kernels, and the step length is set to be 1; the second convolution layer adopts 192 pieces of 5 * 5 convolution kernels, and the step length is set to be 1; 384 pieces of 3 * 3 convolution kernels are adopted in the third convolution layer and the fourth convolution layer, and the step length is set to be 1; 256 pieces of 3 * 3 convolution kernels are adopted in the fifth convolution layer, and the step length is set to be 1; the pooling window of each maximum pooling layer is 2 * 2, and the step length is 2; 1024 nodes are adopted in the full connection layer; the output layer comprises two nodes.

Description

technical field

[0001] This application relates to the field of neural network models, in particular, to a large-scale network burst traffic identification model, method and model training method. Background technique

[0002] With the rapid development of the Internet, it has brought great convenience to people's life, but it has also brought great challenges to network management. Therefore, network traffic identification and distinction has become more and more important. Because it has great potential in solving capacity planning, traffic engineering, fault diagnosis, application performance, anomaly detection, and network trend analysis. Network operators can dynamically deploy QoS (Quality of Service) based on real-time network traffic identification results, and improve network architecture based on analysis results, thereby avoiding network congestion and improving network utilization.

[0003] According to the 45th "Statistical Report on Internet Development in Chi...

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