Abstract
References
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- [3] A. C. Polak, S. Dolatshahi and D. L. Goeckel, "Identifying wireless users via transmitter imperfections," IEEE Journal on selected areas in communications, vol. 29, no. 7, pp. 1469-1479, 2011.
- [4] S. Mathur, A. Reznik, C. Ye, R. Mukherjee, A. Rahman, Y. Shah, W. Trappe and N. Mandayam, "Exploiting the physical layer for enhanced security [security and privacy in emerging wireless networks]," IEEE Wireless Communications, vol. 17, no. 5, pp. 63-70, 2010.
- [5] B. Danev, H. Luecken, S. Capkun and K. El Defrawy, "Attacks on physical-layer identification," in Proceedings of the third ACM conference on Wireless network security, Hoboken, New Jersey, USA, 2010.
- [6] K. Merchant, S. Revay, G. Stantchev and B. Nousain, "Deep learning for RF device fingerprinting in cognitive communication networks," IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 1, pp. 160-167, 2018.
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- [9] J. Toonstra and W. Kinsner, "A radio transmitter fingerprinting system ODO-1," in Proceedings of 1996 Canadian Conference on Electrical and Computer Engineering, Calgary, AB, Canada, 1996.
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- [11] D. Zanetti, B. Danev and S. Capkun, "Physical-layer identification of UHF RFID tags," in Proceedings of the sixteenth annual international conference on Mobile computing and networking, Chicago, IL, USA, 2010.
- [12] S. U. Rehman, K. W. Sowerby and C. Coghill, "Analysis of impersonation attacks on systems using RF fingerprinting and low-end receivers," Journal of Computer and System Sciences, vol. 80, no. 3, pp. 591-601, 2014.
- [13] O. Tekbas, N. Serinken and O. Ureten, "An experimental performance evaluation of a novel radio-transmitter identification system under diverse environmental conditions," Canadian Journal of Electrical and Computer Engineering, vol. 29, no. 3, pp. 203-209, 2004.
- [14] S. Riyaz, K. Sankhe, S. Ioannidis and K. Chowdhury, "Deep learning convolutional neural networks for radio identification," IEEE Communications Magazine, vol. 56, no. 9, pp. 146-152, 2018.
- [15] S. U. Rehman, K. W. Sowerby, S. Alam and I. Ardekani, "Radio frequency fingerprinting and its challenges," in 2014 IEEE Conference on Communications and Network Security, San Francisco, CA, USA, 2014.
- [16] S. Wang, H. Jiang, X. Fang, Y. Ying, J. Li and B. Zhang, "Radio frequency fingerprint identification based on deep complex residual network," IEEE Access, vol. 8, pp. 204417-204424, 2020.
- [17] W. C. Suski II, M. A. Temple, M. J. Mendenhall and R. F. Mills, "Radio frequency fingerprinting commercial communication devices to enhance electronic security," International Journal of Electronic Security and Digital Forensics, vol. 1, no. 3, pp. 301-322, 2008.
- [18] N. Soltanieh, Y. Norouzi, Y. Yang and N. C. Karmakar, "A review of radio frequency fingerprinting techniques," IEEE Journal of Radio Frequency Identification, vol. 4, no. 3, pp. 222-233, 2020.
- [19] D. Shaw and W. Kinsner, "Multifractal modelling of radio transmitter transients for classification," in IEEE WESCANEX 97 Communications, Power and Computing, Winnipeg, MB, Canada, 1997.
- [20] J. Terry and J. Heiskala, OFDM wireless LANs: A theoretical and practical guide, Indianapolis, Indiana, USA: Sams publishing, 2002.
- [21] J. Li, D. Bi, Y. Ying, K. Wei and B. Zhang, "An improved algorithm for extracting subtle features of radiation source individual signals," Electronics, vol. 8, no. 2, p. 246, 2019.
The Effect of Ambient Temperature On Device Classification Based On Radio Frequency Fingerprint Recognition
Abstract
Physical layer authentication is an important technique for cybersecurity, especially in military scenarios. Device classification using radio frequency fingerprinting, which is based on recognizing device-unique characteristics of the transient waveform observed at the beginning of a transmission from a radio device, is a promising method in this context. In this study, the effect of the ambient temperature on the performance of radio device classification based on RF fingerprinting is investigated. The waveforms of the transient regions of the transmissions are recorded as images, and ResNet50 and InceptionV3 networks for image classification are used to determine the radio devices. The radio devices used in the study belong to the same brand, model, and production date, making the problem more difficult than classifying radio devices of different brands or models. Our results show that high levels of accuracy can be attained using convolutional neural network models such as ResNet50 and InceptionV3 when the test data and the training data are collected at the same temperature, whereas performance suffers when the test data and the training data belong to different temperature values. We provide the performance figures of a blended training model that uses training data taken at various temperature values. A comparison of the two networks is also provided.
Keywords
cybersecurity device classification radio frequency fingerprint double sliding window image classification resnet50 inceptionV3
References
- [1] O. Ureten and N. Serinken, "Wireless security through RF fingerprinting," Canadian Journal of Electrical and Computer Engineering, vol. 32, no. 1, pp. 27-33, 2007.
- [2] D. R. Reising, M. A. Temple and M. J. Mendenhall, "Improving intra-cellular security using air monitoring with RF fingerprints," in 2010 IEEE Wireless Communication and Networking Conference, Sydney, NSW, Australia, 2010.
- [3] A. C. Polak, S. Dolatshahi and D. L. Goeckel, "Identifying wireless users via transmitter imperfections," IEEE Journal on selected areas in communications, vol. 29, no. 7, pp. 1469-1479, 2011.
- [4] S. Mathur, A. Reznik, C. Ye, R. Mukherjee, A. Rahman, Y. Shah, W. Trappe and N. Mandayam, "Exploiting the physical layer for enhanced security [security and privacy in emerging wireless networks]," IEEE Wireless Communications, vol. 17, no. 5, pp. 63-70, 2010.
- [5] B. Danev, H. Luecken, S. Capkun and K. El Defrawy, "Attacks on physical-layer identification," in Proceedings of the third ACM conference on Wireless network security, Hoboken, New Jersey, USA, 2010.
- [6] K. Merchant, S. Revay, G. Stantchev and B. Nousain, "Deep learning for RF device fingerprinting in cognitive communication networks," IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 1, pp. 160-167, 2018.
- [7] I. O. Kennedy and A. M. Kuzminskiy, "RF fingerprint detection in a wireless multipath channel," in 7th International Symposium on Wireless Communication Systems, York, UK, 2010.
- [8] O. Ureten and N. Serinken, "Bayesian detection of Wi-Fi transmitter RF fingerprints," Electronics Letters, vol. 41, no. 6, pp. 373-374, 2005.
- [9] J. Toonstra and W. Kinsner, "A radio transmitter fingerprinting system ODO-1," in Proceedings of 1996 Canadian Conference on Electrical and Computer Engineering, Calgary, AB, Canada, 1996.
- [10] M. Woelfle, M. Temple, M. Mullins and M. Mendenhall, "Detecting, identifying and locating bluetooth devices using RF fingerprints," in 2009 Military Communications Conference (MILCOM 2009), Boston, MA, USA, 2009.
- [11] D. Zanetti, B. Danev and S. Capkun, "Physical-layer identification of UHF RFID tags," in Proceedings of the sixteenth annual international conference on Mobile computing and networking, Chicago, IL, USA, 2010.
- [12] S. U. Rehman, K. W. Sowerby and C. Coghill, "Analysis of impersonation attacks on systems using RF fingerprinting and low-end receivers," Journal of Computer and System Sciences, vol. 80, no. 3, pp. 591-601, 2014.
- [13] O. Tekbas, N. Serinken and O. Ureten, "An experimental performance evaluation of a novel radio-transmitter identification system under diverse environmental conditions," Canadian Journal of Electrical and Computer Engineering, vol. 29, no. 3, pp. 203-209, 2004.
- [14] S. Riyaz, K. Sankhe, S. Ioannidis and K. Chowdhury, "Deep learning convolutional neural networks for radio identification," IEEE Communications Magazine, vol. 56, no. 9, pp. 146-152, 2018.
- [15] S. U. Rehman, K. W. Sowerby, S. Alam and I. Ardekani, "Radio frequency fingerprinting and its challenges," in 2014 IEEE Conference on Communications and Network Security, San Francisco, CA, USA, 2014.
- [16] S. Wang, H. Jiang, X. Fang, Y. Ying, J. Li and B. Zhang, "Radio frequency fingerprint identification based on deep complex residual network," IEEE Access, vol. 8, pp. 204417-204424, 2020.
- [17] W. C. Suski II, M. A. Temple, M. J. Mendenhall and R. F. Mills, "Radio frequency fingerprinting commercial communication devices to enhance electronic security," International Journal of Electronic Security and Digital Forensics, vol. 1, no. 3, pp. 301-322, 2008.
- [18] N. Soltanieh, Y. Norouzi, Y. Yang and N. C. Karmakar, "A review of radio frequency fingerprinting techniques," IEEE Journal of Radio Frequency Identification, vol. 4, no. 3, pp. 222-233, 2020.
- [19] D. Shaw and W. Kinsner, "Multifractal modelling of radio transmitter transients for classification," in IEEE WESCANEX 97 Communications, Power and Computing, Winnipeg, MB, Canada, 1997.
- [20] J. Terry and J. Heiskala, OFDM wireless LANs: A theoretical and practical guide, Indianapolis, Indiana, USA: Sams publishing, 2002.
- [21] J. Li, D. Bi, Y. Ying, K. Wei and B. Zhang, "An improved algorithm for extracting subtle features of radiation source individual signals," Electronics, vol. 8, no. 2, p. 246, 2019.
Details
| Primary Language | English |
|---|---|
| Subjects | Artificial Intelligence, Engineering |
| Journal Section | Articles |
| Authors | |
| Publication Date | August 31, 2022 |
| Submission Date | June 30, 2022 |
| Acceptance Date | August 6, 2022 |
| Published in Issue | Year 2022 |
Cited By
Transmitter Identification With Contrastive Learning in Incremental Open-Set Recognition
IEEE Internet of Things Journal
https://doi.org/10.1109/JIOT.2023.3300122
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