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Research on Fingerprint Identification of Wireless Devices Based on Information Fusion

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Abstract

With the rapid development of wireless devices in recent years, the hardware tolerance of wireless devices has gradually become narrowed. Traditional radio frequency fingerprint(RF fingerprint) recognition methods are usually used based on single signal features, which will fail to characterize the subtle differences of wireless devices. Therefore, aiming at the shortcomings of traditional radio frequency fingerprint recognition methods, a multi-segment fusion recognition model is proposed based on D-S evidence theory. The fusion features of time-domain RF-DNA and high-order spectral features are used to obtain more accurate radio frequency fingerprint features. Simulation experiments show that the fusion method can significantly improve the recognition performance of traditional fingerprint recognition methods. When the SNR is higher than 5 dB, with the increasing number of signal fusion segment, the recognition rate of the proposed model will be higher than 99%, which prove that it has a better performance and can be used in practice.

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References

  1. Jia M, Yin Z, Guo Q, Liu G, Xuemai G (2018) Downlink design for spectrum efficient IoT network[J]. IEEE Internet Things J 5(5):3397–3404

    Article  Google Scholar 

  2. Liu, S., Bai, W., Liu, G., Li, W., and Srivastava, H. M. (2018) Parallel fractal compression method for big video data[J]. Complexity, 2016976

  3. Li S, Da Xu L, Zhao S (2018) 5G internet of things: a survey[J]. J Ind Inf Integr 10:1–9

    Google Scholar 

  4. Ahmad I, Kumar T, Liyanage M, Okwuibe J, Ylianttila M, Gurtov A (2018) Overview of 5G security challenges and solutions[J]. IEEE Communications Standards Magazine 2(1):36–43

    Article  Google Scholar 

  5. Liu S, Guo C, Al-Turjman F, Muhammad K, de Albuquerque VHC (2020) Reliability of response region: A novel mechanism in visual tracking by edge computing for IIoT environments[J]. Mech Syst Signal Process 138:106537

    Article  Google Scholar 

  6. Choe HC, Poole CE, Yu AM, Szu HH (2005) Novel identification of intercepted signals from unknown radio transmitters[C]. International Society for Optics and Photonics 1(1):504–517

    Google Scholar 

  7. Neumann, C., Heen, O., Onno, S. (2014). An Empirical Study of Passive 802.11 Device Fingerprinting[C]. In 2012 32nd International Conference on Distributed Computing Systems Workshops, 593–602

  8. Hall, J., Barbeau, M., Kranakis, E. (2003). Detection of transient in radio frequency fingerprinting using signal phase[J]. Wireless and Optical Communications, 13–18

  9. Kennedy IO, Scanlon P, Mullany FJ, Buddhikot, Keith E. Nolan, Thomas W. Rondeau (2008) Radio transmitter fingerprinting: A steady state frequency domain approach[C]. In 2008 IEEE 68th Vehicular Technology Conference, 1–5

  10. Klein RW, Temple MA, Mendenhall MJ (2009) Application of wavelet-based RF fingerprinting to enhance wireless network security[J]. Journal of Communications and Networks 11(6):544–555

    Article  Google Scholar 

  11. Lin Y, Zhu X, Zheng Z, Zheng D, Zhou R (2019) The individual identification method of wireless device based on dimensionality reduction and machine learning[J]. J Supercomput 75(6):3010–3027

    Article  Google Scholar 

  12. Jana S, Kasera SK (2009) On fast and accurate detection of unauthorized wireless access points using clock skews[J]. IEEE Trans Mob Comput 9(3):449–462

    Article  Google Scholar 

  13. Brik, V., Banerjee, S., Gruteser, M., and Oh, S. (2008) Wireless device identification with radiometric signatures[C]. Proceedings of the 14th ACM international conference on Mobile computing and networking. 116–127

  14. Zhang Z, Chang J, Chai M, Tang N (2019) Specific emitter identification based on power amplifier[J]. International Journal of Performability Engineering 15(3):1005–1013

    Google Scholar 

  15. Danev, B., Heydt-Benjamin, T. S., and Capkun, S. (2009) Physical-layer identification of RFID devices[C]. USENIX security symposium, 199–214

  16. Bisio I, Garibotto C, Lavagetto F, Sciarrone A, Zappatore S (2019) Blind detection: advanced techniques for WiFi-based drone surveillance[J]. IEEE Trans Veh Technol 68(1):9318–9946

    Article  Google Scholar 

  17. Lin Y, Li Y, Yin X, Zheng D (2018) Multisensor fault diagnosis modeling based on the evidence theory[J]. IEEE Trans Reliab 67(2):513–521

    Article  Google Scholar 

  18. Zheng D, Xu X, Lin Y, Zhou R (2014) Application of DS evidence fusion method in the fault detection of temperature sensor[J]. Math Probl Eng 1(1):1–6

    Google Scholar 

  19. Gao Z, Dang W, Chaoxu M, Yang Y, Li S, Grebogi C (2017) A novel multiplex network-based sensor information fusion model and its application to industrial multiphase flow system[J]. IEEE Transactions on Industrial Informatics 14(9):3982–3988

    Article  Google Scholar 

  20. Irhoumah M, Pusca R, Lefevre E, Mercier D, Romary R, Demian C (2017) Information fusion with belief functions for detection of interturn short-circuit faults in electrical machines using external flux sensors[J]. IEEE Trans Ind Electron 65(3):2642–2652

    Article  Google Scholar 

  21. Duan Z, Wu T, Guo S, Shao T, Malekian R, Li Z (2018) Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: a review[J]. Int J Adv Manuf Technol 96(1):803–819

    Article  Google Scholar 

  22. Yun Lin, Meiyu Wang, Xianglong Zhou, Guoru Ding, Shiwen Mao (2020) Dynamic Spectrum interaction of UAV flight formation communication with priority: a deep reinforcement learning approach[J]. IEEE Transactions on Cognitive Communications and Networking

  23. Wang H, Guo L, Zheng D, Lin Y (2018) A new method of cognitive signal recognition based on hybrid information entropy and DS evidence theory[J]. Mobile Networks and Applications 23(4):677–685

    Article  Google Scholar 

  24. Venkatesh VR, Pethurub KK, Balakrishnan (2019) Precision centric framework for activity recognition using Dempster Shaffer theory and information fusion algorithm in smart environment[J]. Journal of Intelligent & Fuzzy Systems 36(3):2117–2124

    Article  Google Scholar 

  25. Seo D, Yoo B, Ko H (2018) Information fusion of heterogeneous sensors for enriched personal healthcare activity logging[J]. International Journal of Ad Hoc and Ubiquitous Computing 27(4):256–269

    Article  Google Scholar 

  26. Liu S, Liu G, Zhou H (2019) A robust parallel object tracking method for illumination variations. Mobile Networks and Applications 24(1):5–17

    Article  Google Scholar 

  27. Wang M, Li Z, Huang D, Guo X (2018) Performance analysis of information fusion method based on bell function[J]. International Journal of Performability Engineering 14(4):729–740

    Google Scholar 

  28. Sowa J. (2018) On Logical Modeling of the Information Fusion[C]. Handbook of the 6th World Congress and School on Universal Logic, 82

  29. Bihl TJ, Bauer KW, Temple MA (2016) Feature selection for RF fingerprinting with multiple discriminant analysis and using ZigBee device emissions[J]. IEEE Transactions on Information Forensics and Security 11(8):1862–1874

    Article  Google Scholar 

  30. Shuhua Xu, Benxiong Huang, Yuchun Huang, Zhengguang Xu (2007) Identification of individual radio transmitters based on selected surrounding-line integral bispectra[C]. The 9th International Conference on Advanced Communication Technology, 2:1147–1150

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Acknowledgments

This work is supported by the Natural Science Foundation of Heilongjiang Province(LH2019F005), and the Fundamental Research Funds for the Central Universities (HEUCFJ180801, HEUCF180801, 3072019CF0801 and 3072019CFM0802).

Meantime, all the authors declare that there is no conflict of interests regarding the publication of this article.

We gratefully thank of very useful discussions of reviewers.

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Authors and Affiliations

  1. College of Computer Science and Technology, Harbin Engineering University, Harbin, China

    Qiao Tian

  2. College of Information and Communication Engineering, Harbin Engineering University, Harbin, China

    Qiao Tian, Jicheng Jia & Changbo Hou

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  1. Qiao Tian
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  2. Jicheng Jia
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  3. Changbo Hou
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Correspondence to Changbo Hou.

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Tian, Q., Jia, J. & Hou, C. Research on Fingerprint Identification of Wireless Devices Based on Information Fusion. Mobile Netw Appl 25, 2359–2366 (2020). https://doi.org/10.1007/s11036-020-01613-4

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