Abstract
The use of mobile phones or smartphones has become so widespread that most people rely on them for many services and applications like sending e-mails, checking the bank account, accessing cloud platforms, health monitoring, buying on-line and many other applications where sharing sensitive data is required. As a consequence, security functions are important in the use of smartphones, especially because most of the applications require the identification and authentication of the device in mobility. This is usually achieved through cryptographic systems but recent research studies have also investigated alternative or complementary authentication mechanisms which can be used to strengthen cryptographic methods with multi-factor authentication. In this paper, we investigate the identification and the authentication of smartphones using the intrinsic physical properties of the mobile phones built-in microphones. The possibility to identify a microphone on the basis of features extracted from audio recordings is well known in literature but it is mostly used in forensics studies and usually relies on human voice recordings. On the contrary this paper proposes a smartphone identification and authentication approach by stimulating the built-in microphone with non-voice sounds at different frequencies. An extensive data set of 32 phones was used to evaluate experimentally the proposed approach. On the basis of the proven performance of deep learning techniques, a new Convolutional Neural Network architecture is proposed both for the identification and the authentication purposes. Its performance, in comparison to other machine learning algorithms, is demonstrated in presence of different types of noises (e.g., Gaussian White noise, Babble noise and Street noise). Satisfactory results have been obtained showing that the exploitation of a fingerprint from the microphone sensor is a good choice to assess smartphone distinctiveness.
Highlights
The ability to identify smartphones or mobile phones through their built-in components has been demonstrated in the literature for various types of sensors including Charge Coupled Device (CCD), accelerometers, magnetometers and microphones
The main goal of this paper is to study and develop a methodology to identify mobile phones through the analysis of the signal coming from an on-board sensor like the microphone, with the aim to extract a smartphone fingerprint which is unambiguous and distinctive of one specific device
Different optimization algorithms have been evaluated such as the Root Mean Square Propagation (RMSProp), the Stochastic Gradient Descent with Momentum (SGDM) and Adam in term of classification accuracy varying the Signal Noise Ratio (SNR)
Summary
The ability to identify smartphones or mobile phones (in the rest of the paper the two terms are used with the same meaning) through their built-in components has been demonstrated in the literature for various types of sensors including CCDs, accelerometers, magnetometers and microphones. The main goal of this paper is to study and develop a methodology to identify mobile phones through the analysis of the signal coming from an on-board sensor like the microphone, with the aim to extract a smartphone fingerprint which is unambiguous and distinctive of one specific device. This identification is based on the assumption that the manufacturing process leaves some imperfections on the physical structure of each sensor, the output signal suffer from a systematic distortion, which
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