  In this paper, we show that sensor-based impostor detection with deep
learning can achieve excellent impostor detection accuracy at lower hardware
cost compared to past work on sensor-based user authentication (the inverse
problem) which used more conventional machine learning algorithms. While these
methods use other smartphone users' sensor data to build the (user, non-user)
classification models, we go further to show that using only the legitimate
user's sensor data can still achieve very good accuracy while preserving the
privacy of the user's sensor data (behavioral biometrics). For this use case, a
key contribution is showing that the detection accuracy of a Recurrent Neural
Network (RNN) deep learning model can be significantly improved by comparing
prediction error distributions. This requires generating and comparing
empirical probability distributions, which we show in an efficient hardware
design. Another novel contribution is in the design of SID (Smartphone impostor
Detection), a minimalist hardware accelerator that can be integrated into
future smartphones for efficient impostor detection for different scenarios.
Our SID module can implement many common Machine Learning and Deep Learning
algorithms. SID is also scalable in parallelism and performance and easy to
program. We show an FPGA prototype of SID, which can provide more than enough
performance for real-time impostor detection, with very low hardware complexity
and power consumption (one to two orders of magnitude less than related
performance-oriented FPGA accelerators). We also show that the FPGA
implementation of SID consumes 64.41X less energy than an implementation using
the CPU with a GPU.
