By exploiting the ever-expanding amount of data on smart devices and transforming it to meaningful information, it may be possible to uniquely identify a person via user behavior while interacting with their own smart devices. In previous works, identification of users via biometric properties such as fingerprint, iris has been successfully employed to increase the security of access. However, with the popularity of social networks, identification based on behavior over social networks is emerging as a novel concept. In this paper, We use real traces of data collected over several months. The feature set used in the paper includes location of users, their data usage, number of sessions and session duration for Facebook, Linkedin, WhatsApp, Skype and Twitter applications. The collected feature set is aggregated over time and analyzed using machine learning techniques.
['Fazel Anjomshoa', 'Burak Kantarci', 'Melike Erol-Kantarci', 'Stephanie Schuckers']
A mobile platform for sociability-based continuous identification