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

• Published in: Sensors (Basel, Switzerland) Vol. 16; no. 6; p. 818
• Main Authors: Baldini, Gianmarco, Steri, Gary, Dimc, Franc, Giuliani, Raimondo, Kamnik, Roman
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 03.06.2016; MDPI
• Subjects: Accelerometers; Algorithms; counterfeit; Counterfeiting; Electronics; fingerprinting; Fingerprints; gyroscopes; Inertial; Manufacturing; Mechanical systems; MEMS; Microelectromechanical systems; Public Key Infrastructure; Scanning electron microscopy; Sensors; smartphone; Smartphones; Support vector machines; Slovenia; accelerometers; MEMS; smartphone; counterfeit; fingerprinting; gyroscopes
• DOI: 10.3390/s16060818
• ISSN: 1424-8220

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.