Accuracy evaluation of supervised machine learning classification models for wireless network traffic

International Journal of Communication Networks and Distributed Systems 2022
Elans Grabs, Ernests Pētersons, Dmitry Efrosinin, Aleksandrs Ipatovs, Jānis Klūga, Valentin Sturm

The article contains results of training and testing machine learning models with captured network traffic data. The main goal is to perform classification of video traffic in computer networks. Multiple performance metrics have been evaluated for commonly used classic supervised machine learning algorithms, as well as more advanced convolutional neural network model (for comparison). The article describes in detail the experimental setup, traffic pre-processing procedure, features extraction with different traffic window length and model parameters for training/testing. The article provides some experimental results in form of tables and 3D surface plots. The conclusion of the article summarises the main findings and outlines the future study directions.

Atslēgas vārdi
accuracy; classification models; features extraction; network traffic; performance metrics; statistical parameters; supervised machine learning; traffic intensity; window length; wireless networks; convolutional neural network; CNN.

Grabs, E., Pētersons, E., Efrosinin, D., Ipatovs, A., Klūga, J., Sturm, V. Accuracy evaluation of supervised machine learning classification models for wireless network traffic. International Journal of Communication Networks and Distributed Systems, 2022, Vol. 28, No. 6, 655.-678.lpp.

Publikācijas valoda
English (en)