ABSTRACT
OBJECTIVES: Recognition of mobile applications within encrypted network traffic holds considerable effects across multiple domains, encompassing network administration, security, and digital marketing. The creation of network traffic classifiers capable of adjusting to dynamic and unforeseeable real-world settings presents a tremendous challenge. Presently available datasets exclusively encompass traffic data obtained from a singular network environment, thereby restricting their utility in evaluating the robustness and compatibility of a given model. DATA DESCRIPTION: This dataset was gathered from 60 popular Android applications in five different network scenarios, with the intention of overcoming the limitations of previous datasets. The scenarios were the same in the applications set but differed in terms of Internet service provider (ISP), geographic location, device, application version, and individual users. The traffic was generated through real human interactions on physical devices for 3-15 min. The method used to capture the traffic did not require root privileges on mobile phones and filtered out any background traffic. In total, the collected dataset comprises over 48 million packets, 450K bidirectional flows, and 36 GB of data.
