Smartphone Impostor Detection with Behavioral Data Privacy and Minimalist Hardware Support

by   Guangyuan Hu, et al.

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.



page 5


Smartphone Impostor Detection with Built-in Sensors and Deep Learning

In this paper, we show that sensor-based impostor detection with deep le...

A report on personally identifiable sensor data from smartphone devices

An average smartphone is equipped with an abundance of sensors to provid...

Predicting Floor-Level for 911 Calls with Recurrent Neural Networks and Smartphone Sensor Data

In cities with tall buildings, emergency responders need accurate floor-...

Touchtone leakage attacks via smartphone sensors: mitigation without hardware modification

Smartphone motion sensors provide a concealed mechanism for eavesdroppin...

Recognition of Smartphone User Activity: From A Cyclical Perspective

Smartphones have become an important tool for people's daily lives, whic...

Seq2Seq RNN based Gait Anomaly Detection from Smartphone Acquired Multimodal Motion Data

Smartphones and wearable devices are fast growing technologies that, in ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.