Detection of spoofed identities on smartphones via sociability metrics

The pervasiveness of smartphones equipped with various built-in sensors combined with the capability of serving multiple applications that could access social network information introduces next generation soft biometrics tools that could be used to verify a user's identity through their social behavior. Smart mobile devices can provide multi-modal data acquisition from various social networking applications, and when aggregated, these data can help form highly identifiable behaviometric information. Continuous identification and authentication of users through monitoring social behavior improves detection of identity spoofing. In this paper, we propose a social behaviometric framework to cope with identity spoofing on smartphones. The proposed framework consists of a front-end client module that acquires and provides social networking data to the back-end module which runs online machine learning procedures and provides analytics as a service to the front-end in order to verify user identity through social interactions. We evaluate the performance of the proposed framework by using real data collected from participants, and inject noisy behavioral patterns to simulate identity spoofing scenarios. Performance results show that under anomalous behavioral patterns, the proposed system can identify genuine users with up to 97% success ratio using an aggregated behavior pattern on five different social network applications.