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Sensors 2016, 16(6), 818; doi:10.3390/s16060818

Experimental Identification of Smartphones Using Fingerprints of Built-In Micro-Electro Mechanical Systems (MEMS)

1
European Commission, Joint Research Centre, Ispra 21027, Italy
2
Faculty of Maritime Studies and Transport, University of Ljubljana, Portorož 6320, Slovenia
3
Faculty of Electrical Engineering, University of Ljubljana, Ljubljana SI 1000, Slovenia
*
Author to whom correspondence should be addressed.
Academic Editor: Stefano Mariani
Received: 23 March 2016 / Revised: 26 May 2016 / Accepted: 27 May 2016 / Published: 3 June 2016
(This article belongs to the Collection Modeling, Testing and Reliability Issues in MEMS Engineering)
View Full-Text   |   Download PDF [1844 KB, uploaded 3 June 2016]   |  

Abstract

The correct identification of smartphones has various applications in the field of security or the fight against counterfeiting. As the level of sophistication in counterfeit electronics increases, detection procedures must become more accurate but also not destructive for the smartphone under testing. Some components of the smartphone are more likely to reveal their authenticity even without a physical inspection, since they are characterized by hardware fingerprints detectable by simply examining the data they provide. This is the case of MEMS (Micro Electro-Mechanical Systems) components like accelerometers and gyroscopes, where tiny differences and imprecisions in the manufacturing process determine unique patterns in the data output. In this paper, we present the experimental evaluation of the identification of smartphones through their built-in MEMS components. In our study, three different phones of the same model are subject to repeatable movements (composing a repeatable scenario) using an high precision robotic arm. The measurements from MEMS for each repeatable scenario are collected and analyzed. The identification algorithm is based on the extraction of the statistical features of the collected data for each scenario. The features are used in a support vector machine (SVM) classifier to identify the smartphone. The results of the evaluation are presented for different combinations of features and Inertial Measurement Unit (IMU) outputs, which show that detection accuracy of higher than 90% is achievable. View Full-Text
Keywords: MEMS; fingerprinting; accelerometers; gyroscopes; counterfeit; smartphone MEMS; fingerprinting; accelerometers; gyroscopes; counterfeit; smartphone
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Baldini, G.; Steri, G.; Dimc, F.; Giuliani, R.; Kamnik, R. Experimental Identification of Smartphones Using Fingerprints of Built-In Micro-Electro Mechanical Systems (MEMS). Sensors 2016, 16, 818.

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