  With more and more adoption of Deep Learning (DL) in the field of image
processing, computer vision and NLP, researchers have begun to apply DL to
tackle with encrypted traffic classification problems. Although these methods
can automatically extract traffic features to overcome the difficulty of
traditional classification methods like DPI in terms of feature engineering, a
large amount of data is needed to learn the characteristics of various types of
traffic. Therefore, the performance of classification model always
significantly depends on the quality of datasets. Nevertheless, the building of
datasets is a time-consuming and costly task, especially encrypted traffic
data. Apparently, it is often more difficult to collect a large amount of
traffic samples of those unpopular encrypted applications than well-known,
which leads to the problem of class imbalance between major and minor encrypted
applications in datasets. In this paper, we proposed a novel traffic data
augmenting method called PacketCGAN using Conditional GAN. As a generative
model, PacketCGAN exploit the benefit of CGAN to generate specified traffic to
address the problem of the datasets' imbalance. As a proof of concept, three
classical DL models like Convolutional Neural Network (CNN) were adopted and
designed to classify four encrypted traffic datasets augmented by Random Over
Sampling (ROS), SMOTE(Synthetic Minority Over-sampling Techinique) , vanilla
GAN and PacketCGAN respectively based on two public datasets: ISCX2012 and
USTC-TFC2016. The experimental evaluation results demonstrate that DL based
encrypted traffic classifier over dataset augmented by PacketCGAN can achieve
better performance than the others.
