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Network traffic classification is a significant and problematic aspect of network resource management arising from an investigation of network developments, planning, and design for 5G and beyond. Recently, traffic investigation systems for network monitoring and user access restrictions to Virtual Private Networks (VPN) and non-Virtual Private Networks (non-VPN) have gained widespread attention. In this paper, different algorithms for classifying and detecting VPN traffic are considered. A few existing machine learning procedures were tested concerning their performance in network traffic classification and security. The purpose is to improve Precision, Recall, and F1-score in VPN Network Traffic using Ensemble Classifiers. Therefore, the parameters of the ensemble classifier were changed to obtain high Precision, Recall, and F1-score. Bagging Decision Tree and Gradient Boosting algorithms were used for …