Automatic Mobile Application Traffic Identification by Convolutional Neural Networks

Abstract

Mobile network security and management are becoming important issues, due to the rapid development and widespread of the mobile network. Application traffic identification is a critical technology to resolve these issues. A variety of traffic classification methods on desktop applications are no longer effective in mobile network, because the majority of mobile traffic is carried over HTTP without distinctive features. Existing approaches to identify mobile traffic simply extract obvious features like fixed strings or regular expressions, which are not effective to capture hidden structure within the HTTP headers. In this paper, we propose a novel approach, which can identify mobile application by automatically extracting abstract features from labeled packets. Our approach is mainly based on convolutional neural networks (CNNs). The CNNs can extract the abstract statistical features between characters in HTTP and thus improve the identification accuracy. It's also able to reduce the dependence on prior knowledge and human effort in designing features. To verify the effectiveness of our method, we apply it to several identification tasks. The evaluation shows that our method can accurately identify the traffic of the target mobile application.