DeepMag: Sniffing Mobile Apps in Magnetic Field through Deep Convolutional Neural Networks

In this paper, we report a newfound vulnerability on smartphones due to the malicious use of unsupervised sensor data. We demonstrate that an attacker can train deep Convolutional Neural Networks (CNN) by using magnetometer or orientation data to effectively infer the Apps and their usage informatio...

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Bibliographic Details
Published inProceedings of the IEEE International Conference on Pervasive Computing and Communications pp. 1 - 10
Main Authors Ning, Rui, Wang, Cong, Xin, ChunSheng, Li, Jiang, Wu, Hongyi
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2018
Subjects
Convolution
Light emitting diodes
Magnetic recording
Magnetic resonance imaging
Magnetometers
Smart phones
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ISSN2474-249X
DOI10.1109/PERCOM.2018.8444573

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Summary:In this paper, we report a newfound vulnerability on smartphones due to the malicious use of unsupervised sensor data. We demonstrate that an attacker can train deep Convolutional Neural Networks (CNN) by using magnetometer or orientation data to effectively infer the Apps and their usage information on a smartphone with an accuracy of over 80%. Furthermore, we show that such attacks can become even worse if sophisticated attackers exploit motion sensors to cluster the magnetometer or orientation data, improving the accuracy 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 negligible effect on benign Apps.
ISSN:2474-249X
DOI:10.1109/PERCOM.2018.8444573