Application of machine learning in the classification of traffic in telecommunication networks: working with network modeling systems

Abstract

The issues of classification of online traffic in the framework of the work of network infrastructure modeling systems are considered. The main classifiers C4.5 Decision Tree, Random forest Method, SVM, KNN are considered. The parameters responsible for the speed of the platform are substantiated. The 8CoS model is described. The parameters Accuracy, Sensitivity, Specificity are defined. As part of load testing, a method with the least load on the computing power of the platform, C4.5, was identified. The parameters of the model building time and the general processing time for the case with the number of classification instances up to 2000 are determined. The points at which the C4.5 model gives advantages are identified. Each method was evaluated in terms of classification accuracy and processing time. C4.5 achieved a high percentage of accuracy - 98% with a CPU load of 23.