Abstract
The proposed study will be aimed at improving the accuracy of detecting malicious traffic in telecommunications networks of companies. One of the important tasks of the study was to develop a model of the required network traffic feature space by highlighting the most informative features of network packets. Due to the fact that the analysis of all the features present in network traffic requires large computing resources and the redundancy of the analyzed data can lead to retraining of the developed model, there is an urgent need to reduce the size of the feature space by highlighting the most informative and excluding less informative features. This article proposes the application of the principal component method to achieve the goal of identifying the most informative features from network traffic. The proposed approach increases the reliability of the results of identification of malicious traffic of companies, without loss of quality.