Due to the increase in social media and mobile media in general, access to these platforms from a number of different mobile devices must be catered for. Mobile Device Detection (MDD) refers to software that identifies the type of mobile device visiting the mobile web and either redirects the end user to a dedicated mobile web site or adapts the rendered output from a web server to best suit the capabilities of the end user's mobile device. Furthermore, handset credentials are, in some cases, used for the purpose of correctly identifying the mobile operating system, which in turn is used to redirect to a mobile application download. In any web server request, identification of the user is transmitted in a header field known as the User-Agent (UA). Identifiable information present in the request header allows for unique browser identification and the device used in making the request for a web page. A lookup table, comprising of the all known handset capabilities, is the core functionality of a MDD. Our aim in this paper is to survey the distribution of mobile User-Agents so as to establish an attribution of mobile browsing detections. In particular, assessing details of mobile User-Agents, proxy requests (intermediary for requests from users seeking resources from other servers), emulated requests (software program that imitates a real handset), perpetuates our ability to census mobile traffic with some degree of accuracy. The approach is to make use of a sample set of real-world mobile aggregated requests. We will analyze and filter these requests by origin, User-Agent, geo-location and sometimes non-industry standard (inserted by mobile network operators, which might contain personally identifiable information) to build our mobile browsing framework. In doing so, we encompass description, identification, nomenclature, and classification of end user mobile handset detections. Finally, we will investigate the significance of our framework to see how unique requests are given nothing other than identifiable browser information.
Croft, Neil, "A framework for mobile handset detection" (2016). CONF-IRM 2016 Proceedings. 72.