Evaluation of the Effect of Preprocessing Data on Network Traffic Classifier Based on ML Methods for Qos Predication in Real-Time

The article aims to develop a model for forecasting the characteristics of traffic flows in real-time based on the classification of applications using machine learning methods to ensure the quality of service. It is shown that the model can forecast the mean rate and frequency of packet arrival for the entire flow of each class separately. The prediction is based on information about the previous flows of this class and the first 15 packets of the active flow. Thus, the Random Forest Regression method reduces the prediction error by approximately 1.5 times compared to the standard mean estimate for transmitted packets issued at the switch interface.

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Traffic Flows Forecasting Based on Machine Learning

Deart

Mankov²,

Krasnova

2022

International Journal of Embedded and Real-Time Communication Systems

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Evaluation of the Effect of Preprocessing Data on Network Traffic Classifier Based on ML Methods for Qos Predication in Real-Time

Cited by 1 publication

References 7 publications

Traffic Flows Forecasting Based on Machine Learning

Traffic Flows Forecasting Based on Machine Learning

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