Experimental Identification of Smartphones Using Fingerprints of Built-In Micro-Electro Mechanical Systems (MEMS)
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 testi...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2016-06-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/16/6/818 |
| _version_ | 1798024418298953728 |
|---|---|
| author | Gianmarco Baldini Gary Steri Franc Dimc Raimondo Giuliani Roman Kamnik |
| author_facet | Gianmarco Baldini Gary Steri Franc Dimc Raimondo Giuliani Roman Kamnik |
| author_sort | Gianmarco Baldini |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T18:02:12Z |
| format | Article |
| id | doaj.art-94192557a9164d73aebdb9359e663ba9 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T18:02:12Z |
| publishDate | 2016-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-94192557a9164d73aebdb9359e663ba92022-12-22T04:10:26ZengMDPI AGSensors1424-82202016-06-0116681810.3390/s16060818s16060818Experimental Identification of Smartphones Using Fingerprints of Built-In Micro-Electro Mechanical Systems (MEMS)Gianmarco Baldini0Gary Steri1Franc Dimc2Raimondo Giuliani3Roman Kamnik4European Commission, Joint Research Centre, Ispra 21027, ItalyEuropean Commission, Joint Research Centre, Ispra 21027, ItalyFaculty of Maritime Studies and Transport, University of Ljubljana, Portorož 6320, SloveniaEuropean Commission, Joint Research Centre, Ispra 21027, ItalyFaculty of Electrical Engineering, University of Ljubljana, Ljubljana SI 1000, SloveniaThe 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.http://www.mdpi.com/1424-8220/16/6/818MEMSfingerprintingaccelerometersgyroscopescounterfeitsmartphone |
| spellingShingle | Gianmarco Baldini Gary Steri Franc Dimc Raimondo Giuliani Roman Kamnik Experimental Identification of Smartphones Using Fingerprints of Built-In Micro-Electro Mechanical Systems (MEMS) Sensors MEMS fingerprinting accelerometers gyroscopes counterfeit smartphone |
| title | Experimental Identification of Smartphones Using Fingerprints of Built-In Micro-Electro Mechanical Systems (MEMS) |
| title_full | Experimental Identification of Smartphones Using Fingerprints of Built-In Micro-Electro Mechanical Systems (MEMS) |
| title_fullStr | Experimental Identification of Smartphones Using Fingerprints of Built-In Micro-Electro Mechanical Systems (MEMS) |
| title_full_unstemmed | Experimental Identification of Smartphones Using Fingerprints of Built-In Micro-Electro Mechanical Systems (MEMS) |
| title_short | Experimental Identification of Smartphones Using Fingerprints of Built-In Micro-Electro Mechanical Systems (MEMS) |
| title_sort | experimental identification of smartphones using fingerprints of built in micro electro mechanical systems mems |
| topic | MEMS fingerprinting accelerometers gyroscopes counterfeit smartphone |
| url | http://www.mdpi.com/1424-8220/16/6/818 |
| work_keys_str_mv | AT gianmarcobaldini experimentalidentificationofsmartphonesusingfingerprintsofbuiltinmicroelectromechanicalsystemsmems AT garysteri experimentalidentificationofsmartphonesusingfingerprintsofbuiltinmicroelectromechanicalsystemsmems AT francdimc experimentalidentificationofsmartphonesusingfingerprintsofbuiltinmicroelectromechanicalsystemsmems AT raimondogiuliani experimentalidentificationofsmartphonesusingfingerprintsofbuiltinmicroelectromechanicalsystemsmems AT romankamnik experimentalidentificationofsmartphonesusingfingerprintsofbuiltinmicroelectromechanicalsystemsmems |