Differentiation of Sliding Rescaled Ranges: New Approach to Encrypted and VPN Traffic Detection

December 14, 2020 · Entered Twilight · 🏛 2020 International Conference Engineering and Telecommunication (En&T)

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Repo contents: LICENSE, README.md, VPN_nonVPN.ipynb

Authors Raoul Nigmatullin, Alexander Ivchenko, Semyon Dorokhin arXiv ID 2012.08356 Category cs.CR: Cryptography & Security Cross-listed cs.NI Citations 5 Venue 2020 International Conference Engineering and Telecommunication (En&T) Repository https://github.com/AleksandrIvchenko/dsrr_vpn_nonvpn ⭐ 2 Last Checked 2 months ago

Abstract

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.