  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.
