Document Type

Article

Publication Date

1-2020

DOI

10.1016/j.pmcj.2019.101106

Publication Title

Pervasive and Mobile Computing

Volume

61

Issue

SI

Pages

101106 (16 pp.)

Abstract

This paper reports a new side-channel attack to smartphones using the unrestricted magnetic sensor data. We demonstrate that attackers can effectively infer the Apps being used on a smartphone with an accuracy of over 80%, through training a deep Convolutional Neural Networks (CNN). Various signal processing strategies have been studied for feature extractions, including a tempogram based scheme. Moreover, by further exploiting the unrestricted motion sensor to cluster magnetometer data, the sniffing accuracy can increase to as high as 98%. To mitigate such attacks, we propose a noise injection scheme that can effectively reduce the App sniffing accuracy to only 15% and at the same time has a negligible effect on benign Apps. ©2019 Published by Elsevier B.V.

Comments

©2019 Published by Elsevier B.V.

Funding agencies: Office of Naval Research and National Science Foundation (NSF).

Grant numbers: CNS-1528004, CNS-1659795, CNS-1745632, CNS-1828593, EEC-1840458, OAC-1829771

Original Publication Citation

Ning, R., Wang, C., Xin, C. S., Li, J., & Wu, H. Y. (2020). DeepMag+: Sniffing mobile apps in magnetic field through deep learning. Pervasive and Mobile Computing, 61, 101106. doi:10.1016/j.pmcj.2019.101106

Download

Share

COinS