Mobile behaviometric framework for sociability assessment and identification of smartphone users

Abstract

The widespread use of mobile technology has accelerated the popularity of social networking services, and has made these services convenient to access. This paper presents a behaviometric mobile application, namely TrackMaison (Track My activity in social networks). TrackMaison keeps track of social network service usage of smartphone users through data usage, location, usage frequency and session duration of five popular social network services. The data collected by the mobile application is presented to the smartphone user and is analyzed to aid in understanding mobile social network service usage. Furthermore, we introduce the social activity rate and sociability factor metrics where the former is a function of a user's relative data usage rate in social network services and the latter is a function of a user's relative session durations in social networks. By using TrackMaison tool, we identify three user behavior types. Those are active user profile, moderately active user profile and low active user profile. Through analysis of real data, we advocate that continuous identification/authentication of mobile device users is possible by using the introduced sociability metrics. We further present a case study on various Instagram user profiles, and show that low active profiles can be identified with negligible false acceptance rates (FAR) whereas a highly active user can be identified with a FAR as low as 3%.