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

A technology for burst traffic and model recognition, applied in the field of neural network models, which can solve problems such as time-consuming and labor-intensive

Pending Publication Date: 2021-04-09
ZHEJIANG UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Problems solved by technology

In real life, the amount of network traffic data is huge, and it would be time-consuming and labor-intensive to distinguish and identify them only on a single machine

Method used

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

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Embodiment Construction

[0048] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0049] In order to facilitate the understanding of this program, some relevant theoretical knowledge involved in this application is first introduced here:

[0050] Convolutional neural networks are usually used in the field of image processing, but since Wang W and others first used convolutional neural networks to process and analyze the raw data of network traffic in 2017, and achieved good results in detecting malicious traffic, more and more Many scholars have improved the convolutional neural network and applied it to the classification and identification of network traffic.

[0051] Convolutional neural network is essentially a multi-layer perceptron, which is specially designed to deal with the variability of two-dimensional shape, and it outperforms other techniques. It can guarantee dis...

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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...

Claims

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Application Information

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IPC IPC(8): G06N3/04G06N3/08
CPCG06N3/084G06N3/047G06N3/045
Inventor 云本胜孙雨璐方科彬钱亚冠吴淑慧
Owner ZHEJIANG UNIVERSITY OF SCIENCE AND TECHNOLOGY
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