  In this paper, we introduce a novel end-to-end traffic classification method
to distinguish between traffic classes including VPN traffic in three layers of
the Open Systems Interconnection (OSI) model. Classification of VPN traffic is
not trivial using traditional classification approaches due to its encrypted
nature. We utilize two well-known neural networks, namely multi-layer
perceptron and recurrent neural network to create our cascade neural network
focused on two metrics: class scores and distance from the center of the
classes. Such approach combines extraction, selection, and classification
functionality into a single end-to-end system to systematically learn the
non-linear relationship between input and predicted performance. Therefore, we
could distinguish VPN traffics from non-VPN traffics by rejecting the unrelated
features of the VPN class. Moreover, we obtain the application type of non-VPN
traffics at the same time. The approach is evaluated using the general traffic
dataset ISCX VPN-nonVPN, and an acquired dataset. The results demonstrate the
efficacy of the framework approach for encrypting traffic classification while
also achieving extreme accuracy, $95$ percent, which is higher than the
accuracy of the state-of-the-art models, and strong generalization
capabilities.
