On the Performance of Variational Mode Decomposition-Based Radio Frequency Fingerprinting of Bluetooth Devices
Radio frequency fingerprinting (RFF) is one of the communication network’s security techniques based on the identification of the unique features of RF transient signals. However, extracting these features could be burdensome, due to the nonstationary nature of transient signals. This may...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-03-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/6/1704 |
| _version_ | 1798039234604433408 |
|---|---|
| author | Alghannai Aghnaiya Yaser Dalveren Ali Kara |
| author_facet | Alghannai Aghnaiya Yaser Dalveren Ali Kara |
| author_sort | Alghannai Aghnaiya |
| collection | DOAJ |
| description | Radio frequency fingerprinting (RFF) is one of the communication network’s security techniques based on the identification of the unique features of RF transient signals. However, extracting these features could be burdensome, due to the nonstationary nature of transient signals. This may then adversely affect the accuracy of the identification of devices. Recently, it has been shown that the use of variational mode decomposition (VMD) in extracting features from Bluetooth (BT) transient signals offers an efficient way to improve the classification accuracy. To do this, VMD has been used to decompose transient signals into a series of band-limited modes, and higher order statistical (HOS) features are extracted from reconstructed transient signals. In this study, the performance bounds of VMD in RFF implementation are scrutinized. Firstly, HOS features are extracted from the band-limited modes, and then from the reconstructed transient signals directly. Performance comparison due to both HOS feature sets is presented. Moreover, the lower SNR bound within which the VMD can achieve acceptable accuracy in the classification of BT devices is determined. The approach has been tested experimentally with BT devices by employing a Linear Support Vector Machine (LSVM) classifier. According to the classification results, a higher classification performance is achieved (~4% higher) at lower SNR levels (−5−5 dB) when HOS features are extracted from band-limited modes in the implementation of VMD in RFF of BT devices. |
| first_indexed | 2024-04-11T21:51:05Z |
| format | Article |
| id | doaj.art-32ab6556580f4f8c833806cc0e8d66b0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T21:51:05Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-32ab6556580f4f8c833806cc0e8d66b02022-12-22T04:01:14ZengMDPI AGSensors1424-82202020-03-01206170410.3390/s20061704s20061704On the Performance of Variational Mode Decomposition-Based Radio Frequency Fingerprinting of Bluetooth DevicesAlghannai Aghnaiya0Yaser Dalveren1Ali Kara2Department of Communications Engineering, College of Electronic Technology, Bani Walid, LibyaDepartment of Electronic Systems, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, 2815 Gjøvik, NorwayDepartment of Electrical and Electronics Engineering, Atilim University, Kizilcasar Mah., 06830 Incek, Golbasi, Ankara, TurkeyRadio frequency fingerprinting (RFF) is one of the communication network’s security techniques based on the identification of the unique features of RF transient signals. However, extracting these features could be burdensome, due to the nonstationary nature of transient signals. This may then adversely affect the accuracy of the identification of devices. Recently, it has been shown that the use of variational mode decomposition (VMD) in extracting features from Bluetooth (BT) transient signals offers an efficient way to improve the classification accuracy. To do this, VMD has been used to decompose transient signals into a series of band-limited modes, and higher order statistical (HOS) features are extracted from reconstructed transient signals. In this study, the performance bounds of VMD in RFF implementation are scrutinized. Firstly, HOS features are extracted from the band-limited modes, and then from the reconstructed transient signals directly. Performance comparison due to both HOS feature sets is presented. Moreover, the lower SNR bound within which the VMD can achieve acceptable accuracy in the classification of BT devices is determined. The approach has been tested experimentally with BT devices by employing a Linear Support Vector Machine (LSVM) classifier. According to the classification results, a higher classification performance is achieved (~4% higher) at lower SNR levels (−5−5 dB) when HOS features are extracted from band-limited modes in the implementation of VMD in RFF of BT devices.https://www.mdpi.com/1424-8220/20/6/1704bluetooth signalsfeature extractionrf fingerprintingsignal classificationemitter identificationvariational mode decomposition |
| spellingShingle | Alghannai Aghnaiya Yaser Dalveren Ali Kara On the Performance of Variational Mode Decomposition-Based Radio Frequency Fingerprinting of Bluetooth Devices Sensors bluetooth signals feature extraction rf fingerprinting signal classification emitter identification variational mode decomposition |
| title | On the Performance of Variational Mode Decomposition-Based Radio Frequency Fingerprinting of Bluetooth Devices |
| title_full | On the Performance of Variational Mode Decomposition-Based Radio Frequency Fingerprinting of Bluetooth Devices |
| title_fullStr | On the Performance of Variational Mode Decomposition-Based Radio Frequency Fingerprinting of Bluetooth Devices |
| title_full_unstemmed | On the Performance of Variational Mode Decomposition-Based Radio Frequency Fingerprinting of Bluetooth Devices |
| title_short | On the Performance of Variational Mode Decomposition-Based Radio Frequency Fingerprinting of Bluetooth Devices |
| title_sort | on the performance of variational mode decomposition based radio frequency fingerprinting of bluetooth devices |
| topic | bluetooth signals feature extraction rf fingerprinting signal classification emitter identification variational mode decomposition |
| url | https://www.mdpi.com/1424-8220/20/6/1704 |
| work_keys_str_mv | AT alghannaiaghnaiya ontheperformanceofvariationalmodedecompositionbasedradiofrequencyfingerprintingofbluetoothdevices AT yaserdalveren ontheperformanceofvariationalmodedecompositionbasedradiofrequencyfingerprintingofbluetoothdevices AT alikara ontheperformanceofvariationalmodedecompositionbasedradiofrequencyfingerprintingofbluetoothdevices |