针对设备端口链路的LSTM网络流量预测与链路拥塞方案

doi:10.11959/j.issn.2096-109x.2019066

Abstract

Abstract:

To predict the traffic at single port and single link,two network traffic prediction models based on long short-term memory neural network were proposed.The first model is for the traffic which changes smoothly at large time granularity.The second model is for the nonstationary traffic which fluctuates violently at small time granularity.By selecting different methods of splitting data and training models,two traffic prediction models with different neural network structures were constructed.The experimental results show that the former can achieve a very high accuracy when predicting smoothly changed traffic,the latter has a significantly better prediction effect than the support vector regression model and the back propagation neural network model when dealing with nonstationary traffic.Based on the second model,a link congestion warning scheme with variable parameters was proposed.The scheme is proved to be practicable by experiments.

Key words: long short-term memory (LSTM), machine learning, network traffic prediction, nonstationary traffic prediction, time series prediction

CLC Number:

TP393

Wei HUANG,Cuncai LIU,Sibo QI. LSTM network traffic prediction and link congestion warning scheme for single port and single link[J]. Chinese Journal of Network and Information Security, 2019, 5(6): 50-57.

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模型	Accurate Rate	Unbelievable Rate
SVR	0.913 8	0
BP	0.931	0
LSTM	0.965 5	0.017 2

模型	Accurate Rate	Unbelievable Rate
SVR	0.783 1	0.012
BP	0.819 3	0.024 1
LSTM	0.831 3	0.024 1
LSTM-modified	0.903 6	0.012

模型	Accurate Rate	Unbelievable Rate
SVR	0.494	0.192 8
BP	0.759	0.048 2
LSTM	0.771 1	0.048 2
LSTM-modified	0.722 9	0.060 2

LSTM network traffic prediction and link congestion warning scheme for single port and single link

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