Open Access Open Access  Restricted Access Subscription or Fee Access

Time Reversal Resonating Strength Paradigm for Physical Layer Authentication in Industrial Wireless Communication

Sedki Younis(1*)

(1) Ninevah University, Iraq
(*) Corresponding author


DOI: https://doi.org/10.15866/irecap.v11i3.20768

Abstract


The physical layer authentication, particularly the channel state information approaches, has attracted significant research efforts due to the low computational complexity that makes it a promising solution to provide the security requirements of industrial internet of things and cyber physical systems applications. In the literature, the generalized likelihood ratio test and its many variations have been used to measure the similarity between two channel estimates’. In contrast to the previous research, this paper presents a novel physical layer authentication mechanism based on the time reversal resonating strength of the channel frequency response to formulate the binary hypothesis test of the physical layer authentication. The authentication performance of the proposed time reversal resonating strength based physical layer authentication method is evaluated using real industrial wireless system radio propagation measurements dataset. The simulation results have demonstrated the superior performance of the proposed scheme for detecting the spoofing attack while maintaining minimum false alarm rate when compared with other state-of-the-art techniques.
Copyright © 2021 Praise Worthy Prize - All rights reserved.

Keywords


Industrial Wireless Sensor Networks; Cyber Physical Security; Physical Layer Security; Time Reversal Resonating Strength; Likelihood Ratio Test

Full Text:

PDF


References


F. Pan, Z. Pang, M. Luvisotto, M. Xiao and H. Wen, Physical-Layer Security for Industrial Wireless Control Systems: Basics and Future Directions, IEEE Industrial Electronics Magazine, vol. 12, no. 4, pp. 18-27, Dec. 2018.
https://doi.org/10.1109/mie.2018.2874385

E. Jorswieck, S. Tomasin and A. Sezgin, Broadcasting Into the Uncertainty: Authentication and Confidentiality by Physical-Layer Processing, Proceedings of the IEEE, vol. 103, no. 10, pp. 1702-1724, Oct. 2015.
https://doi.org/10.1109/jproc.2015.2469602

R.-F. Liao, H. Wen, J. Wu, F. Pan, A. Xu, Y. Jiang, F. Xie, and M. Cao, Deep-Learning-Based Physical Layer Authentication for Industrial Wireless Sensor Networks, Sensors, vol. 19, no. 11, p. 2440, May 2019.
https://doi.org/10.3390/s19112440

Wen, H., Wang, Y., Zhu, X., Li, J. and Zhou, L. (2013), Physical layer assist authentication technique for smart meter system. IET Communications, 7: 189-197.
https://doi.org/10.1049/iet-com.2012.0300

Salem, M., Abd Aziz, A., Al-Selwi, H., Bin Alias, M., Geok, T., Mahmud, A., Bin-Ghooth, A., Machine Learning-Based Node Selection for Cooperative Non-Orthogonal Multi-Access System Under Physical Layer Security, (2020) International Journal on Communications Antenna and Propagation (IRECAP), 10 (5), pp. 311-324.
https://doi.org/10.15866/irecap.v10i5.18594

Taqieddin, E., Al-Dahoud, H., Mhaidat, K., Security Analysis and Improvement of Reconstruction Based Radio Frequency Identification Authentication Protocol, (2018) International Journal on Communications Antenna and Propagation (IRECAP), 8 (3), pp. 206-221.
https://doi.org/10.15866/irecap.v8i3.13398

F. Pan et al., Threshold-Free Physical Layer Authentication Based on Machine Learning for Industrial Wireless CPS, IEEE Trans. on Industrial Informatics, vol. 15, no. 12, pp. 6481-6491, Dec. 2019.
https://doi.org/10.1109/tii.2019.2925418

Aldwairi, M., Mardini, W., Alhowaide, A., Anomaly Payload Signature Generation System Based on Efficient Tokenization Methodology, (2018) International Journal on Communications Antenna and Propagation (IRECAP), 8 (5), pp. 421-429.
https://doi.org/10.15866/irecap.v8i5.12794

Z. Gu, H. Chen, P. Xu, Y. Li and B. Vucetic, Physical Layer Authentication for Non-Coherent Massive SIMO-Enabled Industrial IoT Communications, IEEE Transactions on Information Forensics and Security, vol. 15, pp. 3722-3733, 2020.
https://doi.org/10.1109/tifs.2020.2998947

F. Pan et al., Authentication Based on Channel State Information for Industrial Wireless Communications, IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, Washington, DC, USA, 2018, pp. 4125-4130.
https://doi.org/10.1109/iecon.2018.8592783

S. Chen, Z. Pang, H. Wen, Y. Kan and T. Zhang, Physical Layer Authentication Schemes Against Clone Node and Sybil Attacks in Wireless Industrial Internet, 2019 IEEE International Conference on Industrial Internet (ICII), Orlando, FL, USA, 2019, pp. 381-386.
https://doi.org/10.1109/icii.2019.00071

Liu, W. et al. Non-Crypto Authentication for Smart Grid Based on Edge Computing. 6th Annual International Conference on Network and Information Systems for Computers, 2020, Guiyang, China.

A. Weinand, M. Karrenbauer, J. Lianghai and H. D. Schotten, Physical Layer Authentication for Mission Critical Machine Type Communication Using Gaussian Mixture Model Based Clustering, IEEE 85th Vehicular Technology Conference (VTC Spring), Sydney, NSW, Australia, 2017, pp. 1-5.
https://doi.org/10.1109/vtcspring.2017.8108527

C. Pei, N. Zhang, X. S. Shen and J. W. Mark, Channel-based physical layer authentication, 2014 IEEE Global Communications Conference, Austin, TX, USA, 2014, pp. 4114-4119.
https://doi.org/10.1109/glocom.2014.7037452

Z. Wu, Y. Han, Y. Chen and K. J. R. Liu, A Time-Reversal Paradigm for Indoor Positioning System, IEEE Transactions on Vehicular Technology, vol. 64, no. 4, pp. 1331-1339, April 2015.
https://doi.org/10.1109/tvt.2015.2397437

Wu, Zhung-Han, Time-Reversal Indoor Positioning System and Medium Access Control, PhD thesis, University of Maryland, 2017.

Chen, Chen, Radio Analytics for Indoor Localization and Vital Sign Monitoring, PhD thesis, University of Maryland, 2017.

Q. Song, S. Guo, X. Liu and Y. Yang, CSI Amplitude Fingerprinting-Based NB-IoT Indoor Localization, IEEE Internet of Things Journal, vol. 5, no. 3, pp. 1494-1504, June 2018.
https://doi.org/10.1109/jiot.2017.2782479

J. M. Rocamora, I. W. Ho and M. Mak, The Application of Machine Learning Techniques on Channel Frequency Response Based Indoor Positioning in Dynamic Environments, IEEE International Conference on Sensing, Communication and Networking (SECON Workshops), Hong Kong, China, 2018, pp. 1-4.
https://doi.org/10.1109/seconw.2018.8396358

C. Chen, Y. Chen, Y. Han, H. Lai and K. J. R. Liu, Achieving Centimeter-Accuracy Indoor Localization on WiFi Platforms: A Frequency Hopping Approach, IEEE Internet of Things Journal, vol. 4, no. 1, pp. 111-121, Feb. 2017.
https://doi.org/10.1109/jiot.2016.2628701

L. Xiao, L. J. Greenstein, N. B. Mandayam and W. Trappe, Channel-based spoofing detection in frequency-selective rayleigh channels, IEEE Transactions on Wireless Communications, vol. 8, no. 12, pp. 5948-5956, December 2009.
https://doi.org/10.1109/twc.2009.12.081544

A. Mahmood, W. Aman, M. O. Iqbal, M. M. U. Rahman and Q. H. Abbasi, Channel Impulse Response-Based Distributed Physical Layer Authentication, 2017 IEEE 85th Vehicular Technology Conference (VTC Spring), 2017, pp. 1-5.
https://doi.org/10.1109/vtcspring.2017.8108524

R. Candell, K. A. Remley, and N. Moayeri, Radio frequency measurements for selected manufacturing and industrial environments, NIST, Tech. Rep. 1951, 2016. [Online].
Available: http://doi.org/10.18434/T44S3N

Gül, Ö., A Research on Cyber Security Intrusion Detection Against Physical Access Cyber Attacks Using Open Source Software for Smart Grids, (2021) International Review of Electrical Engineering (IREE), 16 (2), pp. 136-146.
https://doi.org/10.15866/iree.v16i2.19174

Angrisani, L., Bonavolontà, F., Dassi, C., Liccardo, A., Schiano Lo Moriello, R., Tocchi, A., On the Suitability of Compressive Sampling for LoRa Signals Classification, (2020) International Review of Electrical Engineering (IREE), 15 (3), pp. 187-198.
https://doi.org/10.15866/iree.v15i3.18129


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2021 Praise Worthy Prize