A Large-Scale Mobile Traffic Dataset For Mobile Application Identification

Abstract With Internet access shifting from desktop-driven to mobile-driven, application-level mobile traffic identification has become a research hotspot. Although considerable progress has been made in this research field, two obstacles are hindering its further development. Firstly, there is a lack of sharable labeled mobile traffic datasets. Although it is easy to capture mobile traffic, labeling traffic at the application level is non-trivial. Besides, researchers usually hold a conservative attitude toward publishing their datasets for privacy concerns. Secondly, most of the datasets used by existing studies are inadequate to evaluate the proposed methods, since they usually have the problems of inaccurate labels, small scale and simple collection configurations. To tackle these two obstacles, a mobile traffic collection is carried out in this paper. The collected traffic has the advantages of large-scale data size, accurate application-level labels and diverse collection configurations. Then, the collected traffic is anonymized carefully to make it public. Several mobile traffic identification methods are compared based on our anonymized dataset, which proves the applicability of our dataset.