RF Fingerprinting of IoT Devices Based on Transient Energy Spectrum
Radio frequency (RF) fingerprinting is considered as one of the promising techniques to enhance wireless security in the Internet of Things (IoT) applications. In this paper, a low-complexity RF fingerprinting method for classification of wireless IoT devices is proposed. The method is based on the...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8631016/ |
| _version_ | 1818618335430967296 |
|---|---|
| author | Memduh Kose Selcuk Tascioglu Ziya Telatar |
| author_facet | Memduh Kose Selcuk Tascioglu Ziya Telatar |
| author_sort | Memduh Kose |
| collection | DOAJ |
| description | Radio frequency (RF) fingerprinting is considered as one of the promising techniques to enhance wireless security in the Internet of Things (IoT) applications. In this paper, a low-complexity RF fingerprinting method for classification of wireless IoT devices is proposed. The method is based on the energy spectrum of the transmitter turn-on transient signals from which unique characteristics of wireless devices are extracted. The number of spectral components to be used is determined through a proposed approach based on the estimated transient duration value. Transient duration estimation is achieved from the smoothed versions of the instantaneous amplitude characteristics of transmitter signals, which are obtained through a sliding windowaveraging method. Classification performance of the proposed spectral fingerprints is assessed using experimental data and described by a confusion matrix. The discrimination effectiveness of the spectral fingerprints is quantified by a class separability criterion and evaluated for different noise levels through Monte Carlo simulations. It is demonstrated that the proposed fingerprints outperform the classification performance of two existing fingerprints especially at low signal-to-noise ratio. Additionally, computational complexity analysis of the classifier using the proposed fingerprints is provided. |
| first_indexed | 2024-12-16T17:19:57Z |
| format | Article |
| id | doaj.art-67b7c7e0e1ea4fe7a908d29d464f3973 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:19:57Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-67b7c7e0e1ea4fe7a908d29d464f39732022-12-21T22:23:11ZengIEEEIEEE Access2169-35362019-01-017187151872610.1109/ACCESS.2019.28966968631016RF Fingerprinting of IoT Devices Based on Transient Energy SpectrumMemduh Kose0https://orcid.org/0000-0002-4935-4542Selcuk Tascioglu1https://orcid.org/0000-0001-9064-2960Ziya Telatar2Computer Sciences Research and Application Center, Kırşehir Ahi Evran University, Kırşehir, TurkeyDepartment of Electrical and Electronics Engineering, Ankara University, Ankara, TurkeyDepartment of Electrical and Electronics Engineering, Ankara University, Ankara, TurkeyRadio frequency (RF) fingerprinting is considered as one of the promising techniques to enhance wireless security in the Internet of Things (IoT) applications. In this paper, a low-complexity RF fingerprinting method for classification of wireless IoT devices is proposed. The method is based on the energy spectrum of the transmitter turn-on transient signals from which unique characteristics of wireless devices are extracted. The number of spectral components to be used is determined through a proposed approach based on the estimated transient duration value. Transient duration estimation is achieved from the smoothed versions of the instantaneous amplitude characteristics of transmitter signals, which are obtained through a sliding windowaveraging method. Classification performance of the proposed spectral fingerprints is assessed using experimental data and described by a confusion matrix. The discrimination effectiveness of the spectral fingerprints is quantified by a class separability criterion and evaluated for different noise levels through Monte Carlo simulations. It is demonstrated that the proposed fingerprints outperform the classification performance of two existing fingerprints especially at low signal-to-noise ratio. Additionally, computational complexity analysis of the classifier using the proposed fingerprints is provided.https://ieeexplore.ieee.org/document/8631016/Internet of Things (IoT) securityradio transmitter turn-on transientRF fingerprintingtransient energy spectrumwireless device identification |
| spellingShingle | Memduh Kose Selcuk Tascioglu Ziya Telatar RF Fingerprinting of IoT Devices Based on Transient Energy Spectrum IEEE Access Internet of Things (IoT) security radio transmitter turn-on transient RF fingerprinting transient energy spectrum wireless device identification |
| title | RF Fingerprinting of IoT Devices Based on Transient Energy Spectrum |
| title_full | RF Fingerprinting of IoT Devices Based on Transient Energy Spectrum |
| title_fullStr | RF Fingerprinting of IoT Devices Based on Transient Energy Spectrum |
| title_full_unstemmed | RF Fingerprinting of IoT Devices Based on Transient Energy Spectrum |
| title_short | RF Fingerprinting of IoT Devices Based on Transient Energy Spectrum |
| title_sort | rf fingerprinting of iot devices based on transient energy spectrum |
| topic | Internet of Things (IoT) security radio transmitter turn-on transient RF fingerprinting transient energy spectrum wireless device identification |
| url | https://ieeexplore.ieee.org/document/8631016/ |
| work_keys_str_mv | AT memduhkose rffingerprintingofiotdevicesbasedontransientenergyspectrum AT selcuktascioglu rffingerprintingofiotdevicesbasedontransientenergyspectrum AT ziyatelatar rffingerprintingofiotdevicesbasedontransientenergyspectrum |