  Internet traffic classification has become more important with rapid growth
of current Internet network and online applications. There have been numerous
studies on this topic which have led to many different approaches. Most of
these approaches use predefined features extracted by an expert in order to
classify network traffic. In contrast, in this study, we propose a \emph{deep
learning} based approach which integrates both feature extraction and
classification phases into one system. Our proposed scheme, called "Deep
Packet," can handle both \emph{traffic characterization} in which the network
traffic is categorized into major classes (\eg, FTP and P2P) and application
identification in which end-user applications (\eg, BitTorrent and Skype)
identification is desired. Contrary to most of the current methods, Deep Packet
can identify encrypted traffic and also distinguishes between VPN and non-VPN
network traffic. After an initial pre-processing phase on data, packets are fed
into Deep Packet framework that embeds stacked autoencoder and convolution
neural network in order to classify network traffic. Deep packet with CNN as
its classification model achieved recall of $0.98$ in application
identification task and $0.94$ in traffic categorization task. To the best of
our knowledge, Deep Packet outperforms all of the proposed classification
methods on UNB ISCX VPN-nonVPN dataset.
