CAEN: A Deep Learning Approach to Mobile App Traffic Identification

Abstract

Mobile app traffic now accounts for a majority owing to the booming mobile devices and mobile apps. State-of-the-art identification methods, such as DPI and flow-based classifiers, have difficulties in designing features and labeling samples manually. Motivated by the excellence of CNNs in visual object recognition, we propose convolutional autoencoder network (CAEN), a deep learning approach to mobile app traffic identification. Our contributions are two-fold. First, we propose a novel method of converting traffic flows into vision-meaningful images, and thus enable the machine to identify the traffic in a human way. Based on the method, we create an open dataset named IMTD. Second, convolutional autoencoder (CAE) algorithm is introduced into the proposed network model, realizing the automatic feature extraction and the learning from massive unlabeled samples. The experimental results show that the identification accuracy of our approach can reach 99.5%, which satisfies the practical requirement.