Hybrid feature learning framework for the classification of encrypted network traffic

Abstract

The purpose of traffic classification is to allocate bandwidth to different types of data on a network. Application-level traffic classification is important for identifying the applications that are in high demand on the network. Due to the increasing complexity and volume of internet traffic, machine learning and deep learning methods are being used more frequently in traffic classification. The focus of this research is to evaluate the performance of the Support Vector Machine (SVM) in classifying network packets by application type, as well as classifying the type of data communicated within an application. The research considers encrypted network packets, including those from Virtual Private Networks (VPN) and the WhatsApp mobile application. Previous research has shown that deep learning methods are effective in the feature learning process, so this study uses a simple feed-forward Deep Neural Network (DNN) to improve the performance of the SVM algorithm. Additionally, various feature learning frameworks based on deep learning, such as DNN, Autoencoder and PCA, are compared. The study concludes that the DNN is able to improve the F1 score of the SVM classifier from 0.78 to 0.90. Furthermore, the study shows that using a hybrid framework of DNN with SVM can address the class imbalance problem often present in machine learning.