Traffic Identification of Mobile Apps Based on Variational Autoencoder Network

Abstract

Traffic identification is a fundamental issue in network security. Traditional methods, such as depth packet inspection (DPI) and flow-based classifiers, have difficulties in labeling massive samples and extracting features manually. Motivated by the achievements in computer vision, we focus on mobile app traffic, proposing a deep learning model based on variational autoencoder network (VEAN). Our contributions are two-fold. First, we propose a novel method of transforming mobile app traffic flows into vision-meaningful images, and thus enable the machine to identify the traffic in a human way. Then, based on the transformation method, we create an open dataset named IMTD17. Second, an improved network model is proposed, where variational autoencoder (VAE) algorithm is introduced into a two-stage learning. The model realizes the learning from massive unlabeled data, and the feasibility of the replacement for manual feature extraction is illustrated by the visualization analysis of the latent features. The experimental results show that the identification accuracy can reach 99.6%, which satisfies the practical requirement.