Smartphone Impostor Detection with Behavioral Data Privacy and Minimalist Hardware Support

Guangyuan Hu, Zecheng He, Ruby B. Lee

Keywords: Anomaly detection, deep learning, hardware accelerator, security

TL;DR: Privacy-preserving impostor detection with deep learning and the hardware support.

Abstract: 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 that can 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. To make the on-device, real-time detection possible, we show how a minimalist hardware module, dubbed SID for Smartphone Imposter 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.