Encrypted 5G Over- The- Top Voice Traffic Identification Based on Deep Learning
Zhuang Qiao, Liuqun Zhai, Shunliang Zhang, Xiaohui Zhang · 2021 IEEE Symposium on Computers and Communications (ISCC) · 2021
With the commercialization of fifth-generation (5G), the rapid popularity of mobile Over- The- Top (OTT) voice applications brings huge impacts on the traditional telecommunications voice call service. Tunnel encryption and anonymous network technologies allow OTT users to escape the supervision of network operators easily, which may cause potential security risks to cyberspace. To monitor harmful OTT applications in the context of 5G, it is critical to identify encrypted OTT voice traffic. However, there is no comprehensive study on typical OTT voice traffic identification. This is the first study to analyze OTT Virtual Private Network (VPN) voice traffic in the 5G network specifically. We propose to employ Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) to classify encrypted 5G OTT VPN voice traffic, study the impact of the sample sizes and the deep learning methods on identification performance. To verify the performance of the proposed approach, we collect 10 types of typical OTT VPN voice traffic from the experimental 5G network. Extensive experimental results prove the effectiveness of the proposed approach in encrypted 5G OTT VPN voice traffic classification.