Title: Multi-sensor authentication to improve smartphone security,
Abstract: The widespread use of smartphones gives rise to new security and privacy
concerns. Smartphone thefts account for the largest percentage of thefts in
recent crime statistics. Using a victim's smartphone, the attacker can launch
impersonation attacks, which threaten the security of the victim and other
users in the network. Our threat model includes the attacker taking over the
phone after the user has logged on with his password or pin. Our goal is to
design a mechanism for smartphones to better authenticate the current user,
continuously and implicitly, and raise alerts when necessary. In this paper, we
propose a multi-sensors-based system to achieve continuous and implicit
authentication for smartphone users. The system continuously learns the owner's
behavior patterns and environment characteristics, and then authenticates the
current user without interrupting user-smartphone interactions. Our method can
adaptively update a user's model considering the temporal change of user's
patterns. Experimental results show that our method is efficient, requiring
less than 10 seconds to train the model and 20 seconds to detect the abnormal
user, while achieving high accuracy (more than 90%). Also the combination of
more sensors provide better accuracy. Furthermore, our method enables adjusting
the security level by changing the sampling rate.