Real-time Traffic Classification in Encrypted Wireless Communication Network

Classification of traffic service types is a valuable function for wireless communication networks. Even though some progress has been made, the recognition of the type of the traffic services cannot be done in real time. In this paper, we propose a novel method for classifying traffic series in real time based on transfer learning techniques. We pre-train a deep learning model with long traffic series and fine-tune the model with short traffic series. In this way, the developed model achieves the capability of recognising traffic services in real time. In other words, the model can recognize traffic services by using short traffic series. We collect Downlink Control Information (DCI) from commercial LTE networks when using five common types of traffic services. Then we use the dataset to validate our method. Our experimental results show that, by using proposed method, LSTM accuracy rates will increase to 80% and 88.5% when the length of the traffic series is 5 seconds and 10 seconds respectively, which is higher than the baseline. The strategy is also suitable for one dimension convolution neural network (1D-CNN).