A New Encrypted Traffic Identification Model Based on VAE-LSTM-DRN

Abstract

Encrypted traffic identification pertains to the precise acquisition and categorization of data from traffic datasets containing imbalanced and obscured content. The extraction of encrypted traffic attributes and their subsequent identification presents a formidable challenge. The existing models have predominantly relied on direct extraction of encrypted traffic data from imbalanced datasets, with the dataset's imbalance significantly affecting the model's performance. In the present study, a new model, referred to as UD-VLD (Unbalanced Dataset-VAE-LSTM-DRN), was proposed to address above problem. The proposed model is an encrypted traffic identification model for handling unbalanced datasets. The encoder of the variational autoencoder (VAE) is combined with the decoder and Long-short term Memory (LSTM) in UD-VLD model to realize the data enhancement processing of the original unbalanced datasets. The enhanced data is processed by transforming the deep residual network (DRN) to address neural network gradient-related issues. Subsequently, the data is classified and recognized. The UD-VLD model integrates the related techniques of deep learning into the encrypted traffic recognition technique, thereby solving the processing problem for unbalanced datasets. The UD-VLD model was tested using the publicly available Tor dataset and VPN dataset. The UD-VLD model is evaluated against other comparative models in terms of accuracy, loss rate, precision, recall, F1-score, total time, and ROC curve. The results reveal that the UD-VLD model exhibits better performance in both binary and multi classification, being higher than other encrypted traffic recognition models that exist for unbalanced datasets. Furthermore, the evaluation performance indicates that the UD-VLD model effectively mitigates the impact of unbalanced data on traffic classification. and can serve as a novel solution for encrypted traffic identification.