论文标题
滑动重新定化范围的差异：加密和VPN交通检测的新方法
Differentiation of Sliding Rescaled Ranges: New Approach to Encrypted and VPN Traffic Detection
论文作者
论文摘要
我们提出了一种新的方法来进行交通预处理，称为滑动重新定化的范围（DSRR），扩大了H.E.提出的想法。赫斯特。我们在著名的ISCXVPN2016数据集上采用了建议的方法对特征的加密和未加密流量。我们将DSRR用于流量库功能，然后使用基本的机器学习模型求解任务VPN与NonVPN。使用DSRR和随机森林，我们获得了0.971的精度，0.969召回并将该结果提高到0.976，使用特征与神经网络方法相比，该特征通过2D-CNN提供了0.93精度。提出的方法和结果可以在https://github.com/aleksandrivchenko/dsrr_vpn_nonvpn上找到。
We propose a new approach to traffic preprocessing called Differentiation of Sliding Rescaled Ranges (DSRR) expanding the ideas laid down by H.E. Hurst. We apply proposed approach on the characterizing encrypted and unencrypted traffic on the well-known ISCXVPN2016 dataset. We deploy DSRR for flow-base features and then solve the task VPN vs nonVPN with basic machine learning models. With DSRR and Random Forest, we obtain 0.971 Precision, 0.969 Recall and improve this result to 0.976 using statistical analysis of features in comparison with Neural Network approach that gives 0.93 Precision via 2D-CNN. The proposed method and the results can be found at https://github.com/AleksandrIvchenko/dsrr_vpn_nonvpn.
