Accuracy evaluation of supervised machine learning classification models for wireless network traffic. (25th August 2022)

• Record Type: Journal Article
• Authors: Grabs, Elans; Petersons, Ernests; Efrosinin, Dmitry; Ipatovs, Aleksandrs; Kluga, Janis; Sturm, Valentin
• Is Part Of: International journal of communication networks and distributed systems. Volume 28:Number 6(2022)
• Page Start: 655
• Page End: 678
• Publication Date: 2022-08-25
• Subjects: accuracy -- classification models -- features extraction -- network traffic -- performance metrics -- statistical parameters -- supervised machine learning -- traffic intensity -- window length -- wireless networks -- convolutional neural network -- CNN; Computer networks -- Periodicals; Telecommunication systems -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; 004.6
• Languages: English
• ISSNs: 1754-3916

Abstract

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 the form of tables and 3D surface plots. The conclusion of the article summarises the main findings and outlines the future study directions.