Smartphones Identification Through the Built-In Microphones With Convolutional Neural Network
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...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8888157/ |
| _version_ | 1818412545995702272 |
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| author | Gianmarco Baldini Irene Amerini |
| author_facet | Gianmarco Baldini Irene Amerini |
| author_sort | Gianmarco Baldini |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T10:49:01Z |
| format | Article |
| id | doaj.art-ad8cc1424c5b4389bb2fa6e8b6fe74b9 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T10:49:01Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ad8cc1424c5b4389bb2fa6e8b6fe74b92022-12-21T23:05:20ZengIEEEIEEE Access2169-35362019-01-01715868515869610.1109/ACCESS.2019.29508598888157Smartphones Identification Through the Built-In Microphones With Convolutional Neural NetworkGianmarco Baldini0https://orcid.org/0000-0003-4830-1227Irene Amerini1European Commission, Joint Research Centre, Ispra, ItalyDepartment of Computer, Control, and Management Engineering A. Ruberti, Sapienza University of Rome, Rome, ItalyThe 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.https://ieeexplore.ieee.org/document/8888157/Smartphone identificationauthenticationmicrophonemachine learningdeep learning |
| spellingShingle | Gianmarco Baldini Irene Amerini Smartphones Identification Through the Built-In Microphones With Convolutional Neural Network IEEE Access Smartphone identification authentication microphone machine learning deep learning |
| title | Smartphones Identification Through the Built-In Microphones With Convolutional Neural Network |
| title_full | Smartphones Identification Through the Built-In Microphones With Convolutional Neural Network |
| title_fullStr | Smartphones Identification Through the Built-In Microphones With Convolutional Neural Network |
| title_full_unstemmed | Smartphones Identification Through the Built-In Microphones With Convolutional Neural Network |
| title_short | Smartphones Identification Through the Built-In Microphones With Convolutional Neural Network |
| title_sort | smartphones identification through the built in microphones with convolutional neural network |
| topic | Smartphone identification authentication microphone machine learning deep learning |
| url | https://ieeexplore.ieee.org/document/8888157/ |
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