Using Social Group Trajectories for Potential Impersonation Detection on Smart Buildings Access Control

In many domains, applications use people trajectories' spatiotemporal data for impersonation fraud detection purposes. Anomaly-based approaches consist in constructing mobility profiles based on users' frequent paths and schedules and comparing new trajectories against these profiles: if some new trajectory is not consistent with the user profile, a potential impersonation is detected. Previous studies, however, do not include traveling companions in users' profiles, although performing activities in social groups is inherent to human behavior. Physical access control systems on smart buildings can provide activity companions information since social groups naturally emerge on organizations hosted in such buildings and these systems can capture group trajectories. This paper explores the feasibility of using spatiotemporal mobility profiles enriched with group trajectory pattern data as a novel framework for impersonation fraud detection in smart buildings. An empirical analysis is conducted, and results show that it is feasible to add companions activities information to mobility profiles in order to enhance anomaly-based impersonation attack detection.