Devices and methods for smartphone impostor detection using behavioral and environmental data

Abstract

Devices and methods for smartphone impostor detection using behavioral and environmental data are provided. Impostors are attackers who take control of an electronic device (e.g., a smartphone) and gain access to confidential and private information of a legitimate user. Embodiments described herein propose a defense-in-depth mechanism to detect impostors quickly with simple deep learning algorithms, which can achieve better detection accuracy than previous works. Embodiments then consider protecting the privacy of the behavioral and/or environmental data (e.g., collected by one or more sensors) of a user by not exposing it outside the protected device. A recurrent neural network (RNN)-based deep learning algorithm is proposed which uses only sensor data of the legitimate user to learn their normal behavior. Prediction error distribution (PED) is used to enhance the detection accuracy. In some embodiments, a minimalist hardware module, dubbed smartphone impostor detector (SID), is integrated into smartphones for self-contained impostor detection.