RF-TESI: Radio Frequency Fingerprint-based Smartphone Identification under Temperature Variation
Article No.: 41, Pages 1 - 21
Abstract
Radio frequency fingerprint identification (RFFI) is a promising technique for smartphone identification. However, we find that the temperature of the RF front end in smartphones can significantly impact the RF features, including the carrier frequency offset (CFO) and statistical RF features. The unstable RF features caused by temperature changes can negatively affect the performance of state-of-the-art RFFI approaches. To this end, we propose the RF-TESI solution for smartphone identification under temperature variation. First, we construct a dataset by extracting temperature and RF features. In the dataset, the extracted temperature values constitute a set of temperature values and each registered temperature value corresponds to a group of RF features. Next, we evaluate the distinctiveness of RF features across smartphones to select the most suitable RF fingerprint. Then, we train multiple random forest models, each tagged with a registered temperature. In addition, because there are still many temperatures out of the temperature set, we design an RF fingerprint estimation method to estimate RF fingerprints at unregistered temperatures. Finally, the experiments show RF-TESI demonstrates satisfactory performance under different scenarios, taking into account variations in temperature, time and position. Besides, our proposed approach is better than all state-of-the-art approaches in smartphone identification.
References
[1]
Ralf Achenbach, Martin Feuerstack-Raible, Friedrich Hiller, Michael Keller, Karlheinz Meier, Harald Rudolph, and Roland Saur-Brosch. 2000. A digitally temperature-compensated crystal oscillator. IEEE J. Solid State Circuits 35, 10 (2000), 1502–1506.
[2]
Amani Al-Shawabka, Francesco Restuccia, Salvatore D’Oro, Tong Jian, Bruno Costa Rendon, Nasim Soltani, Jennifer Dy, Stratis Ioannidis, Kaushik Chowdhury, and Tommaso Melodia. 2020. Exposing the fingerprint: Dissecting the impact of the wireless channel on radio fingerprinting. In IEEE INFOCOM 2020-IEEE Conference on Computer Communications. IEEE, 646–655.
[3]
Kiam Heong Ang, Gregory Chong, and Yun Li. 2005. PID control system analysis, design, and technology. IEEE Transactions on Control Systems Technology 13, 4 (2005), 559–576.
[4]
Krishna B. Athreya and Soumendra N. Lahiri. 2006. Measure Theory and Probability Theory. Vol. 19. Springer.
[5]
Vladimir Brik, Suman Banerjee, Marco Gruteser, and Sangho Oh. 2008. Wireless device identification with radiometric signatures. In Proceedings of the 14th ACM International Conference on Mobile Computing and Networking. 116–127.
[6]
Lars Buitinck, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, Olivier Grisel, Vlad Niculae, Peter Prettenhofer, Alexandre Gramfort, Jaques Grobler, Robert Layton, Jake VanderPlas, Arnaud Joly, Brian Holt, and Gaël Varoquaux. 2013. API design for machine learning software: Experiences from the scikit-learn project. In ECML PKDD Workshop: Languages for Data Mining and Machine Learning. 108–122.
[7]
Shihua Cai and Keyong Li. 2022. MATLAB Implementation of Wavelet Transforms. https://eeweb.engineering.nyu.edu/iselesni/WaveletSoftware/dt1D.html.
[8]
Aaron Carroll and Gernot Heiser. 2010. An analysis of power consumption in a smartphone. In USENIX Annual Technical Conference. USENIX Association.
[9]
Myung-Jun Choe, Kwang-Hyun Baek, and Mesfin Teshome. 2005. A 1.6-GS/s 12-bit return-to-zero GaAs RF DAC for multiple Nyquist operation. IEEE J. Solid State Circuits 40, 12 (2005), 2456–2468.
[10]
Android Developing Official Document. 2023. Sensors Overview.
[11]
EMR. 2023. Global Smartphones Market Share, Size, Growth, Trends, Analysis, Forecast.
[12]
Mohamed K. M. Fadul, Donald R. Reising, and Mina Sartipi. 2021. Identification of OFDM-based radios under Rayleigh fading using RF-DNA and deep learning. IEEE Access 9 (2021), 17100–17113.
[13]
Hans Fischer. 2010. A History of the Central Limit Theorem: From Classical to Modern Probability Theory. Springer Science & Business Media.
[14]
Marvin Frerking. 2012. Crystal Oscillator Design and Temperature Compensation. Springer Science & Business Media.
[15]
Xiaolin Gu, Wenjia Wu, Naixuan Guo, Wei He, Aibo Song, Ming Yang, Zhen Ling, and Junzhou Luo. 2022. TeRFF: Temperature-aware radio frequency fingerprinting for smartphones. In 19th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2022, Stockholm, Sweden, September 20–23, 2022. 127–135.
[16]
Weikun Hou, Xianbin Wang, Jean-Yves Chouinard, and Ahmed Refaey. 2014. Physical layer authentication for mobile systems with time-varying carrier frequency offsets. IEEE Trans. Commun. 62, 5 (2014), 1658–1667.
[17]
Jingyu Hua, Hongyi Sun, Zhenyu Shen, Zhiyun Qian, and Sheng Zhong. 2018. Accurate and efficient wireless device fingerprinting using channel state information. In IEEE INFOCOM 2018-IEEE Conference on Computer Communications. IEEE, 1700–1708.
[18]
Kyungho Joo, Wonsuk Choi, and Dong Hoon Lee. 2020. Hold the door! Fingerprinting your car key to prevent keyless entry car theft. In 27th Annual Network and Distributed System Security Symposium, NDSS 2020, San Diego, California, USA, February 23–26, 2020. The Internet Society.
[19]
Randall W. Klein, Michael A. Temple, and Michael J. Mendenhall. 2009. Application of wavelet-based RF fingerprinting to enhance wireless network security. Journal of Communications and Networks 11, 6 (2009), 544–555.
[20]
Guyue Li, Jiabao Yu, Yuexiu Xing, and Aiqun Hu. 2019. Location-invariant physical layer identification approach for WiFi devices. IEEE Access 7 (2019), 106974–106986.
[21]
Haipeng Li, Kaustubh Gupta, Chenggang Wang, Nirnimesh Ghose, and Boyang Wang. 2022. RadioNet: Robust deep-learning based radio fingerprinting. In 10th IEEE Conference on Communications and Network Security, CNS 2022, Austin, TX, USA, October 3–5, 2022. IEEE, 190–198.
[22]
Qian Lin, Haipeng Fu, Qianfu Cheng, and Yuanyuan Zhu. 2016. Study of the index failure for power amplifier caused by temperature. Analog Integrated Circuits and Signal Processing 89 (2016), 177–183.
[23]
Francesca Meneghello, Michele Rossi, and Francesco Restuccia. 2022. DeepCSI: Rethinking Wi-Fi radio fingerprinting through MU-MIMO CSI feedback deep learning. In 42nd IEEE International Conference on Distributed Computing Systems, ICDCS 2022, Bologna, Italy, July 10–13, 2022. IEEE, 1062–1072.
[24]
Linning Peng, Aiqun Hu, Junqing Zhang, Yu Jiang, Jiabao Yu, and Yan Yan. 2019. Design of a hybrid RF fingerprint extraction and device classification scheme. IEEE Internet Things J. 6, 1 (2019), 349–360.
[25]
Zdeněk Průša, Peter L. Søndergaard, Nicki Holighaus, Christoph Wiesmeyr, and Peter Balazs. 2014. The large time-frequency analysis toolbox 2.0. In Sound, Music, and Motion. Springer International Publishing, 419–442.
[26]
Donald R. Reising, Michael A. Temple, and Michael J. Mendenhall. 2010. Improved wireless security for GMSK-based devices using RF fingerprinting. International Journal of Electronic Security and Digital Forensics 3, 1 (2010), 41–59.
[27]
Donald R. Reising, Michael A. Temple, and Mark E. Oxley. 2012. Gabor-based RF-DNA fingerprinting for classifying 802.16 e WiMAX mobile subscribers. In 2012 International Conference on Computing, Networking and Communications (ICNC). IEEE, 7–13.
[28]
Kunal Sankhe, Mauro Belgiovine, Fan Zhou, Luca Angioloni, Frank Restuccia, Salvatore D’Oro, Tommaso Melodia, Stratis Ioannidis, and Kaushik Chowdhury. 2019. No radio left behind: Radio fingerprinting through deep learning of physical-layer hardware impairments. IEEE Transactions on Cognitive Communications and Networking 6, 1 (2019), 165–178.
[29]
Kunal Sankhe, Mauro Belgiovine, Fan Zhou, Shamnaz Riyaz, Stratis Ioannidis, and Kaushik Chowdhury. 2019. ORACLE: Optimized radio classification through convolutional neural networks. In IEEE INFOCOM 2019-IEEE Conference on Computer Communications. IEEE, 370–378.
[30]
Guanxiong Shen, Junqing Zhang, Alan Marshall, and Joseph R. Cavallaro. 2022. Towards scalable and channel-robust radio frequency fingerprint identification for LoRa. IEEE Transactions on Information Forensics and Security 17 (2022), 774–787.
[31]
Guanxiong Shen, Junqing Zhang, Alan Marshall, Linning Peng, and Xianbin Wang. 2021. Radio frequency fingerprint identification for LoRa using spectrogram and CNN. In IEEE INFOCOM 2021-IEEE Conference on Computer Communications. IEEE, 1–10.
[32]
William C. Suski II, Michael A. Temple, Michael J. Mendenhall, and Robert F. Mills. 2008. Using spectral fingerprints to improve wireless network security. In IEEE GLOBECOM 2008-2008 IEEE Global Telecommunications Conference. IEEE, 1–5.
[33]
Tien Dang Vo-Huu, Triet Dang Vo-Huu, and Guevara Noubir. 2016. Fingerprinting Wi-Fi devices using software defined radios. In Proceedings of the 9th ACM Conference on Security & Privacy in Wireless and Mobile Networks. 3–14.
[34]
Joel H. K. Vuolevi, Timo Rahkonen, and Jani P. A. Manninen. 2001. Measurement technique for characterizing memory effects in RF power amplifiers. IEEE Transactions on Microwave Theory and Techniques 49, 8 (2001), 1383–1389.
[35]
Charles G. Wheeler and Donald R. Reising. 2017. Assessment of the impact of CFO on RF-DNA fingerprint classification performance. In 2017 International Conference on Computing, Networking and Communications, ICNC 2017, Silicon Valley, CA, USA, January 26–29, 2017. IEEE Computer Society, 110–114.
[36]
Wikipedia. 2022. Orthogonal Frequency-division Multiplexing. https://en.wikipedia.org/wiki/Orthogonal_frequency-division_multiplexing
[37]
McKay D. Williams, Sheldon A. Munns, Michael A. Temple, and Michael J. Mendenhall. 2010. RF-DNA fingerprinting for airport WiMax communications security. In 2010 Fourth International Conference on Network and System Security. IEEE, 32–39.
[38]
Renjie Xie, Wei Xu, Yanzhi Chen, Jiabao Yu, Aiqun Hu, Derrick Wing Kwan Ng, and A. Lee Swindlehurst. 2021. A generalizable model-and-data driven approach for open-set RFF authentication. IEEE Transactions on Information Forensics and Security 16 (2021), 4435–4450.
[39]
Wenqing Yan, Thiemo Voigt, and Christian Rohner. 2022. RRF: A robust radiometric fingerprint system that embraces wireless channel diversity. In WiSec ’22: 15th ACM Conference on Security and Privacy in Wireless and Mobile Networks, San Antonio, TX, USA, May 16–19, 2022, Murtuza Jadliwala, Yongdae Kim, and Alexandra Dmitrienko (Eds.). ACM, 85–97.
[40]
Jiabao Yu, Aiqun Hu, Guyue Li, and Linning Peng. 2019. A robust RF fingerprinting approach using multisampling convolutional neural network. IEEE Internet of Things Journal 6, 4 (2019), 6786–6799.
[41]
Yulong Zou, Jia Zhu, Xianbin Wang, and Lajos Hanzo. 2016. A survey on wireless security: Technical challenges, recent advances, and future trends. Proc. IEEE 104, 9 (2016), 1727–1765.
Index Terms
- RF-TESI: Radio Frequency Fingerprint-based Smartphone Identification under Temperature Variation
Recommendations
TEA-RFFI: Temperature adjusted radio frequency fingerprint-based smartphone identification
AbstractRecently, radio frequency fingerprints (RFFs) have been widely applied for smartphone identification, since RFFs are distinguishable and hard to imitate. Compared with other types of RFFs, carrier frequency offset (CFO) in Wi-Fi signals is more ...
Joint estimation of carrier frequency offset and in-phase/quadrature-phase imbalances for orthogonal frequency division multiplexing systems
Orthogonal frequency division multiplexing (OFDM) has been adopted in various high-speed wireless communication systems. OFDM systems using direct-conversion transceivers are gaining much interest because of low cost and power. However, such systems can ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Journal Family
Publication History
Published: 10 January 2024
Online AM: 07 December 2023
Accepted: 01 December 2023
Revised: 25 August 2023
Received: 25 April 2023
Published in TOSN Volume 20, Issue 2
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
- National Natural Science Foundation of China
- Key R&D Program of Jiangsu Province
- Jiangsu Provincial Key Laboratory of Network and Information Security
- Key Laboratory of Computer Network and Information Integration of the Ministry of Education of China
- Fundamental Research Funds for the Central Universities
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 290Total Downloads
- Downloads (Last 12 months)290
- Downloads (Last 6 weeks)28
Reflects downloads up to 19 Nov 2024
Other Metrics
Citations
Cited By
View allView Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in