Benchmark Data for Mobile App Traffic Research
Ruoyu Wang, Zhen Liu, Yongming Cai, Deyu Tang, Jin Ho Yang, Yang Zhao · 2018
Mobile app traffic classification aims to automatically map mobile packets into apps. It has become an active task in mobile traffic engineering, and numerous algorithms have been proposed for this task, including machine learning, deep packet inspection methods. However, existing works mainly evaluate their methods on their own collected mobile traffic traces. There is no public benchmark data. The results in existing papers cannot be directly compared. This largely limits the development of mobile app traffic classification methods. This paper describes our Mobile Traffic Data(MTD): Android app traffic flow sample sets with ground truth. The goal of MTD is to advance the state-of-arts in mobile app traffic classification. For building MTD, we collected and annotated more than ten thousands of traffic flows using Mobilegt system. The popularity used flow features were also extracted to build flow samples for mobile traffic classification using machine learning. MTD sets have been shared in public. In addition, this paper provides the performance analysis of typical machine learning techniques on MTD, which can be served as the baseline results on this benchmark data.