Network Traffic Classification Using Deep Learning Networks and Bayesian Data Fusion

The rapid growth of current computer networks and their applications has made network traffic classification more important. The latest approach in this field is the use of deep learning. But the problem of deep learning is that it needs a lot of data for training. On the other hand, the lack of a sufficient amount of data for different types of network traffic has a negative effect on the accuracy of the traffic classification. In this regard, one of the appropriate solutions to address this challenge is the use of data fusion methods in decision level. Data fusion techniques make possible to achieve better results by combining classifiers. In this paper, a network traffic classification approach based on deep learning and data fusion techniques is presented. The proposed method can identify encrypted traffic and distinguish between VPN and non-VPN network traffic. In the proposed approach, first, a preprocessing on the dataset is carried out, then three deep learning networks, namely, Deep Belief Network, Convolution Neural Network, and Multi-layer Perceptron to classify network traffic are employed. Finally, the results of all three classifiers using Bayesian decision fusion are combined. The experimental results on the ISCX VPN-nonVPN dataset show that the proposed method improves the classification accuracy and performs well on different network traffic types. The average accuracy of the proposed method is 97%.