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 classification which produced promising results when compared to single classifiers like $k$-Nearest Neighbors (kNN), Multilayer Perceptron (MLP), and Decision Tree. The proposed classifier demonstrates recognition accuracy on a test sample of up to $93.80\%$ which outperforms all other single algorithms used in previous work. The MLP, Random Forest (RF), and Gradient Boosting (GB) algorithms had almost identical performance in all experiments. Furthermore, the proposed classifiers are found to perform better when the network traffic flows are generated using different values of time parameters (timeout). Our results show that the ensemble algorithms (Random Forest and the Gradient Boosting) outperform the single machine learning classifier previously used by other researchers, and we achieved the highest accuracy with the random forest classifier with better results while using non-VPN traffic and VPN traffic. The novelty lies in the application of an ensemble algorithm to secure a network traffic classification performed in comparison with single classifiers to determine Accuracy, Precision, and F1-score of a given dataset, contrary to the known process of selection of features and generation.

网络流量分类是网络资源管理的重要且有问题的方面，源于对5G及更高版本的网络开发，规划和设计的调查。近来，用于网络监视和用户对虚拟专用网络（VPN）和非虚拟专用网络（non-VPN）的访问限制的流量调查系统受到了广泛的关注。在本文中，考虑了用于分类和检测VPN流量的不同算法。测试了一些现有的机器学习过程，这些过程涉及它们在网络流量分类和安全性方面的性能。目的是使用Ensemble分类器提高VPN网络流量中的Precision，Recall和F1分数。因此，更改集合分类器的参数以获得高精度，召回率和F1得分。$ķ$-最近邻居（kNN），多层感知器（MLP）和决策树。拟议的分类器展示了对多达2,000个测试样本的识别准确性$93.80％$优于先前工作中使用的所有其他单一算法。MLP，随机森林（RF）和梯度增强（GB）算法在所有实验中的性能几乎相同。此外，发现当使用不同时间参数值（超时）生成网络流量时，建议的分类器性能更好。我们的结果表明，集成算法（Random Forest和Gradient Boosting）的性能优于其他研究人员以前使用的单机器学习分类器，并且在使用非VPN流量和VPN流量的情况下，我们使用随机森林分类器获得了最高的准确度和更好的结果。新颖之处在于应用集成算法来确保与单个分类器相比确定的网络流量分类的准确性，精确度，