Mmost systems of network traffic identification are based on features. The
features may be port numbers, static signatures, statistic characteristics, and so on. The difficulty
of the traffic identification is to find the features in the flow data. The process is very
time‐consuming. Also, these approaches are invalid to unknown protocol. To solve these
problems, we propose a method that is based on neural network and deep learning – a hotspot
of research in machine learning. The results show that our approach works very well on the
applications of feature learning, protocol identification and anomalous protocol detection.
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