Name
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.pmcj.2019.101106 | Pervasive and Mobile Computing |
Keywords | Field | DocType |
Deep learning,Privacy,Magnetic,Smartphone | Signal processing,Magnetic field,Computer science,Convolutional neural network,Sniffing,Computer network,Magnetometer,Real-time computing,Artificial intelligence,Motion sensors,Deep learning,Mobile apps | Journal |
Volume | ISSN | Citations |
61 | 1574-1192 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |