Network Traffic and Ensemble Models in Machine Learning

Currently, the number of network devices and communications is growing rapidly. At the same time, the number and quality of attacks against them is also constantly increasing. To protect devices from malicious activity, various threat detection tools are constantly being developed and improved. This article presents the development of an application for traffic classification based on ensemble machine learning methods. When implementing the application, two algorithms were used - Random Forest and AdaBoost. The accuracy of these algorithms when testing the application was 99.99% and 99.97%, respectively.