Encrypted Voice Traffic Fingerprinting: An Adaptive Network Traffic Feature Encoding Model

Tianci Zhou, Yong Zeng, Yitao Chen, Zhihong Liu, Jianfeng Ma

Abstract: Recent studies show that the traffic fingerprints constructed by encrypted voice traffic features will uncover users' activities. Current researches have focused on using machine learning models to build traffic fingerprints, however, they pay less attention to the distribution of traffic data. We note that encrypted voice traffic data is bimodal distributed, which is different from other encrypted traffic following Gaussian distribution. It means that present encoding model may destroy the distribution characteristics of encrypted voice traffic data, leading to a decrease in classification accuracy. To ameliorate this issue, our study proposes the combination of Discrete Fourier Transform and Stacked Autoencoder as traffic feature encoding model. The former is able to map the encrypted voice traffic features from bimodal distribution to frequency-domain space while keeping the trend of its features. And the latter can further improve features utilization in classification model. Finally, we achieved 96.08% accuracy on Amazon Echo dataset and 99.53% accuracy on Google Home dataset, which get the SOTA(state of the art) results on both encrypted voice traffic datasets.