Name
Abstract | ||
|---|---|---|
Mobile phones identification through their built in components has been demonstrated in literature for various types of sensors including the camera, microphones and accelerometers. The identification is performed by the exploitation of the small but significant differences in the electronic circuits generated during the production process. Thus, these differences become an intrinsic property of the electronic components, which can be detected and become an unique fingerprint of the component and of the mobile phone. In this paper, we investigate the identification of mobile phones through their builtin magnetometers, which has not been reported in literature yet. Magnetometers are stimulated with different waveforms using a solenoid connected to a computer s audio board. The identification is performed analyzing the digital output of the magnetometer through the use of statistical features and the Support Vector Machine (SVM) machine learning algorithm. We prove that this technique can distinguish different models and brands with very high accuracy but it can only distinguish phones of the same model with limited accuracy. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Cryptography and Security | Intrinsic and extrinsic properties (philosophy),Computer science,Magnetometer,Theoretical computer science,Real-time computing,Artificial intelligence,Mobile phone,Computer vision,Accelerometer,Support vector machine,Fingerprint,Electronic circuit,Electronic component |
DocType | Volume | Citations |
Journal | abs/1701.07676 | 1 |
PageRank | References | Authors |
0.34 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gianmarco Baldini | 1 | 465 | 49.94 |
| Gary Steri | 2 | 133 | 10.49 |
| Raimondo Giuliani | 3 | 24 | 5.69 |
| Vladimir Kyovtorov | 4 | 2 | 1.39 |