  With the rapid development of Green Communication Network, the types and
quantity of network traffic data are accordingly increasing. Network traffic
classification become a non-trivial research task in the area of network
management and security, which not only help to improve the fine-grained
network resource allocation, but also enable policy-driven network management.
Meanwhile, the combination of SDN and Edge Computing can leverage both SDN at
its global visiability of network-wide and Edge Computing at its low latency
and good privacy-preserving. However, capturing large labeled datasets is a
cumbersome and time-consuming manual labor. Semi-Supervised learning is an
appropriate technique to overcome this problem. With that in mind, we proposed
a Generative Adversarial Network (GAN)-based Semi-Supervised Learning Encrypted
Traffic Classification method called \emph{ByteSGAN} embedded in SDN Edge
Gateway to achieve the goal of traffic classification in a fine-grained manner
to further improve network resource utilization. ByteSGAN can only use a small
number of labeled traffic samples and a large number of unlabeled samples to
achieve a good performance of traffic classification by modifying the structure
and loss function of the regular GAN discriminator network in a semi-supervised
learning way. Based on public dataset 'ISCX2012 VPN-nonVPN', two experimental
results show that the ByteSGAN can efficiently improve the performance of
traffic classifier and outperform the other supervised learning method like
CNN.
