Identifying Known and Unknown Mobile Application Traffic Using a Multilevel Classifier

Abstract

Due to the proliferation of mobile applications, mobile traffic identification plays a crucial role in understanding the network traffic. However, the pervasive unconcerned apps and the emerging apps pose great challenges to the mobile traffic identification method based on supervised machine learning, since such method merely identifies and discriminates several apps of interest. In this paper we propose a three-layer classifier using machine learning to identify mobile traffic in open-world settings. The proposed method has the capability of identifying traffic generated by unconcerned apps and zero-day apps; thus it can be applied in the real world. A self-collected dataset that contains 160 apps is used to validate the proposed method. The experimental results show that our classifier achieves over 98% precision and produces a much smaller number of false positives than that of the state of the art.