Abstract:
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 Conv...Show MoreMetadata
Abstract:
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.
Date of Conference: 19-23 March 2018
Date Added to IEEE Xplore: 23 August 2018
ISBN Information:
Electronic ISSN: 2474-249X