  Impostors are attackers who take over a smartphone and gain access to the
legitimate user's confidential and private information. This paper proposes a
defense-in-depth mechanism to detect impostors quickly with simple Deep
Learning algorithms, which can achieve better detection accuracy than the best
prior work which used Machine Learning algorithms requiring computation of
multiple features. Different from previous work, we then consider protecting
the privacy of a user's behavioral (sensor) data by not exposing it outside the
smartphone. For this scenario, we propose a Recurrent Neural Network (RNN)
based Deep Learning algorithm that uses only the legitimate user's sensor data
to learn his/her normal behavior. We propose to use Prediction Error
Distribution (PED) to enhance the detection accuracy. We also show how a
minimalist hardware module, dubbed SID for Smartphone Impostor Detector, can be
designed and integrated into smartphones for self-contained impostor detection.
Experimental results show that SID can support real-time impostor detection, at
a very low hardware cost and energy consumption, compared to other RNN
accelerators.
