Encrypted Network Traffic Classification Using Deep and Parallel Network-in-Network Models

Abstract

Network traffic classification aims to recognize different application or traffic types by analyzing received data packets. This paper presents a neural network model with deep and parallel network-in-network (NIN) structures for classifying encrypted network traffic. Comparing with standard convolutional neural networks (CNN), NIN adopts a micro network after each convolution layer to enhance local modeling. Besides, NIN utilizes a global average pooling instead of traditional fully connected layers before final classification, which reduces the number of model parameters significantly. In our proposed method, deep NIN models with multiple MLP convolutional layers are built to map fixed-length packet vectors towards application or traffic labels. Furthermore, a parallel decision strategy of building two sub-networks to process packet header and packet body separately is designed considering that they may carry different kinds of clues for classification. The results of our experiments on the “ISCX VPN-nonVPN” encrypted traffic dataset show that NIN models can achieve a better balance between classification accuracy and model complexity than conventional CNNs. The parallel decision strategy can further improve the accuracy of using single NIN model for encrypted network traffic classification. Finally, the test set F1 scores of 0.983 and 0.985 are achieved for traffic characterization and application identification respectively.