Mobile Traffic Classification Over VPN - Evaluating Encrypted Traffic Classification Techniques on VPN Traffic: A Comparative Study

Abstract

In recent years, mobile network traffic classification has gained significant attention from network operators to better understand customer needs and allocate bandwidth based on application requirements. Research on machine learning and deep learning models has increased in popularity, as these methods enable more accurate classification while leveraging different aspects of network packets beyond just the payload. The primary goal of this thesis is to compare the performance of different state-of-theart deep learning models namely, Convolutional Neural Networks (CNNs), Recurrent Neural Networks(RNNs), and Autoencoders(AEs) through a series of experiments and to evaluate the feasibility of deploying these models for network classification for mobile traffic in VPN environments. The study focuses on network packets that are both encrypted and tunneled over Virtual Private Networks (VPN). A dataset of 50GB of VPN data is used to train, assess, and enhance analysis and training of the models. Our results indicate that CNNs effectively extract features but struggle with capturing sequential dependencies. By comparison, RNNs demonstrate greater efficiency in recognising temporal patterns and achieve higher recall rates. Autoencoders perform well for specific application classes but exhibit lower precision and recall overall. This thesis suggests further investigation into a combined approach between convolutional neural networks and recurrent neural networks to be used for traffic classification over VPNs.