  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.
