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Fatal attraction: identifying mobile devices through electromagnetic emissions

Perez, B; Musolesi, M; Stringhini, G; (2019) Fatal attraction: identifying mobile devices through electromagnetic emissions. In: WiSec '19: Proceedings of the 12th Conference on Security and Privacy in Wireless and Mobile Networks. (pp. pp. 163-173). Association for Computing Machinery (ACM): New York, NY, USA. Green open access

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Abstract

Smartphones are increasingly augmented with sensors for a variety of purposes. In this paper, we show how magnetic field emissions can be used to fingerprint smartphones. Previous work on identification rely on specific characteristics that vary with the settings and components available on a device. This limits the number of devices on which one approach is effective. By contrast, all electronic devices emit a magnetic field which is accessible either through the API or measured through an external device. We conducted an in-the-wild study over four months and collected mobile sensor data from 175 devices. In our experiments we observed that the electromagnetic field measured by the magnetometer identifies devices with an accuracy of 98.9%. Furthermore, we show that even if the sensor was removed from the device or access to it was discontinued, identification would still be possible from a secondary device in close proximity to the target. Our findings suggest that the magnetic field emitted by smartphones is unique and fingerprinting devices based on this feature can be performed without the knowledge or cooperation of users.

Type: Proceedings paper
Title: Fatal attraction: identifying mobile devices through electromagnetic emissions
Event: 12th Conference on Security and Privacy in Wireless and Mobile Networks (WiSec '19)
Location: Miami, FL
Dates: 15 May 2019 - 17 May 2019
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/3317549.3319726
Publisher version: https://doi.org/10.1145/3317549.3319726
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: fngerprint, magnetic feld, sensor readings, supervised learning
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Geography
URI: https://discovery.ucl.ac.uk/id/eprint/10082389
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