Identification of Mobile Phones Using the Built-In Magnetometers Stimulated by Motion PatternsReportar como inadecuado

Identification of Mobile Phones Using the Built-In Magnetometers Stimulated by Motion Patterns

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European Commission, Joint Research Centre, Ispra 21027, Italy


DiSTA, University of Insubria, Varese 21100, Italy


Faculty of Maritime Studies and Transport, University of Ljubljana, Portorož 6320, Slovenia


Faculty of Electrical Engineering, University of Ljubljana, Ljubljana SI 1000, Slovenia


Author to whom correspondence should be addressed.

Academic Editors: Nian X. Sun, Ming Liu and Menghui Li

Abstract We investigate the identification of mobile phones through their built-in magnetometers. These electronic components have started to be widely deployed in mass market phones in recent years, and they can be exploited to uniquely identify mobile phones due their physical differences, which appear in the digital output generated by them. This is similar to approaches reported in the literature for other components of the mobile phone, including the digital camera, the microphones or their RF transmission components. In this paper, the identification is performed through an inexpensive device made up of a platform that rotates the mobile phone under test and a fixed magnet positioned on the edge of the rotating platform. When the mobile phone passes in front of the fixed magnet, the built-in magnetometer is stimulated, and its digital output is recorded and analyzed. For each mobile phone, the experiment is repeated over six different days to ensure consistency in the results. A total of 10 phones of different brands and models or of the same model were used in our experiment. The digital output from the magnetometers is synchronized and correlated, and statistical features are extracted to generate a fingerprint of the built-in magnetometer and, consequently, of the mobile phone. A SVM machine learning algorithm is used to classify the mobile phones on the basis of the extracted statistical features. Our results show that inter-model classification i.e., different models and brands classification is possible with great accuracy, but intra-model i.e., phones with different serial numbers and same model classification is more challenging, the resulting accuracy being just slightly above random choice. View Full-Text

Keywords: fingerprinting; magnetometers; mobile phone fingerprinting; magnetometers; mobile phone

Autor: Gianmarco Baldini 1,2,* , Franc Dimc 3, Roman Kamnik 4, Gary Steri 1, Raimondo Giuliani 1 and Claudio Gentile 2



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