Encrypted traffic classification based on fusion of vision transformer and temporal features

Aiming at the problem that the current encrypted traffic classification methods only use the single network framework such as convolutional neural network (CNN), recurrent neural network (RNN), and stacked autoencoder (SAE), and only construct a shallow network to extract features, which leads to the low accuracy of encrypted traffic classification, an encrypted traffic classification framework based on the fusion of vision transformer and temporal features was proposed. Bottleneck transformer network (BoTNet) was used to extract spatial features and bi-directional long short-term memory (BiLSTM) was used to extract temporal features. After the two subnetworks are parallelized, the feature fusion method of early fusion was used in the framework to perform feature fusion. Finally, the encrypted traffic was identified through the fused features. The experimental results show that the BiLSTM and BoTNet fusion transformer (BTFT) model can enhance the performance of encrypted traffic classification by fusing multi-dimensional features. The accuracy rate of a virtual private network (VPN) and non-VPN binary classification is 99.9%, and the accuracy rate of fine-grained encrypted traffic twelve-classification can also reach 97%. © 2023, Beijing University of Posts and Telecommunications. All rights reserved.