Commenced in January 2007
Paper Count: 30761
Specific Emitter Identification Based on Refined Composite Multiscale Dispersion Entropy
Abstract:The wireless communication network is developing rapidly, thus the wireless security becomes more and more important. Specific emitter identification (SEI) is an vital part of wireless communication security as a technique to identify the unique transmitters. In this paper, a SEI method based on multiscale dispersion entropy (MDE) and refined composite multiscale dispersion entropy (RCMDE) is proposed. The algorithms of MDE and RCMDE are used to extract features for identification of five wireless devices and cross-validation support vector machine (CV-SVM) is used as the classifier. The experimental results show that the total identification accuracy is 99.3%, even at low signal-to-noise ratio(SNR) of 5dB, which proves that MDE and RCMDE can describe the communication signal series well. In addition, compared with other methods, the proposed method is effective and provides better accuracy and stability for SEI.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.3298707Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 224
 K. I. Talbot, P. R. Duley, and M. H. Hyatt, “Specific emitter identification and verification,” Technology Review, vol. 113, 2003.
 Z. Li, Y. Yin, and L. Wu, “Radio frequency fingerprint identification method in wireless communication,” in International Conference on Machine Learning and Intelligent Communications. Springer, 2017, pp. 195–202.
 I. O. Kennedy, P. Scanlon, F. J. Mullany, M. M. Buddhikot, K. E. Nolan, and T. W. Rondeau, “Radio transmitter fingerprinting: A steady state frequency domain approach,” in 2008 IEEE 68th Vehicular Technology Conference. IEEE, 2008, pp. 1–5.
 H. C. Choe, C. E. Poole, M. Y. Andrea, and H. H. Szu, “Novel identification of intercepted signals from unknown radio transmitters,” in Wavelet Applications II, vol. 2491. International Society for Optics and Photonics, 1995, pp. 504–518.
 J. Toonstra and W. Kinsner, “Transient analysis and genetic algorithms for classification,” in IEEE WESCANEX 95. Communications, Power, and Computing. Conference Proceedings, vol. 2. IEEE, 1995, pp. 432–437.
 R. Klein, M. A. Temple, M. J. Mendenhall, and D. R. Reising, “Sensitivity analysis of burst detection and rf fingerprinting classification performance,” in 2009 IEEE International Conference on Communications. IEEE, 2009, pp. 1–5.
 W. C. Suski II, M. A. Temple, M. J. Mendenhall, and R. F. Mills, “Using spectral fingerprints to improve wireless network security,” in IEEE GLOBECOM 2008-2008 IEEE Global Telecommunications Conference. IEEE, 2008, pp. 1–5.
 S. U. Rehman, K. Sowerby, and C. Coghill, “Rf fingerprint extraction from the energy envelope of an instantaneous transient signal,” in 2012 Australian Communications Theory Workshop (AusCTW). IEEE, 2012, pp. 90–95.
 H. Yuan and A. Hu, “Preamble-based detection of wi-fi transmitter rf fingerprints,” Electronics letters, vol. 46, no. 16, pp. 1165–1167, 2010.
 P. Padilla, J. Padilla, and J. Valenzuela-Valdes, “Radio frequency identification of wireless devices based on rf fingerprinting,” Electronics Letters, vol. 49, no. 22, pp. 1409–1410, 2013.
 A. Candore, O. Kocabas, and F. Koushanfar, “Robust stable radiometric fingerprinting for wireless devices,” in 2009 IEEE International Workshop on Hardware-Oriented Security and Trust. IEEE, 2009, pp. 43–49.
 Y. Huang and H. Zheng, “Radio frequency fingerprinting based on the constellation errors,” in 2012 18th Asia-Pacific Conference on Communications (APCC). IEEE, 2012, pp. 900–905.
 V. Brik, S. Banerjee, M. Gruteser, and S. Oh, “Wireless device identification with radiometric signatures,” in Proceedings of the 14th ACM international conference on Mobile computing and networking. ACM, 2008, pp. 116–127.
 R. W. Klein, M. A. Temple, and M. J. Mendenhall, “Application of wavelet-based rf fingerprinting to enhance wireless network security,” Journal of Communications and Networks, vol. 11, no. 6, pp. 544–555, 2009.
 C. Bertoncini, K. Rudd, B. Nousain, and M. Hinders, “Wavelet fingerprinting of radio-frequency identification (rfid) tags,” IEEE Transactions on Industrial Electronics, vol. 59, no. 12, pp. 4843–4850, 2012.
 L. Xuan-min, Y. Ju, and Z. Ya-jian, “A new method based on local integral bispectra and svm for radio transmitter individual identification,” in 2010 WASE international conference on information engineering, vol. 4. IEEE, 2010, pp. 65–68.
 S. Xu, L. Xu, Z. Xu, and B. Huang, “Individual radio transmitter identification based on spurious modulation characteristics of signal envelop,” in MILCOM 2008-2008 IEEE Military Communications Conference. IEEE, 2008, pp. 1–5.
 T. Carroll, “A nonlinear dynamics method for signal identification,” Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 17, no. 2, p. 023109, 2007.
 J. S. Richman and J. R. Moorman, “Physiological time-series analysis using approximate entropy and sample entropy,” American Journal of Physiology-Heart and Circulatory Physiology, vol. 278, no. 6, pp. H2039–H2049, 2000.
 M. Rostaghi and H. Azami, “Dispersion entropy: A measure for time-series analysis,” IEEE Signal Processing Letters, vol. 23, no. 5, pp. 610–614, 2016.
 Y. Xie, S. Wang, E. Zhang, and Z. Zhao, “Specific emitter identification based on nonlinear complexity of signal,” in 2016 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC). IEEE, 2016, pp. 1–6.
 G. Baldini, R. Giuliani, G. Steri, and R. Neisse, “Physical layer authentication of internet of things wireless devices through permutation and dispersion entropy,” in 2017 Global Internet of Things Summit (GIoTS). IEEE, 2017, pp. 1–6.
 S. Deng, Z. Huang, X. Wang, and G. Huang, “Radio frequency fingerprint extraction based on multidimension permutation entropy,” International Journal of Antennas and Propagation, vol. 2017, 2017.
 G. Huang, Y. Yuan, X. Wang, and Z. Huang, “Specific emitter identification based on nonlinear dynamical characteristics,” Canadian Journal of Electrical and Computer Engineering, vol. 39, no. 1, pp. 34–41, 2016.
 H. Azami, E. Kinney-Lang, A. Ebied, A. Fern´andez, and J. Escudero, “Multiscale dispersion entropy for the regional analysis of resting-state magnetoencephalogram complexity in alzheimer’s disease,” in 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2017, pp. 3182–3185.
 H. Azami, M. Rostaghi, D. Ab´asolo, and J. Escudero, “Refined composite multiscale dispersion entropy and its application to biomedical signals,” IEEE Transactions on Biomedical Engineering, vol. 64, no. 12, pp. 2872–2879, 2017.
 M. Costa, A. L. Goldberger, and C.-K. Peng, “Multiscale entropy analysis of biological signals,” Physical review E, vol. 71, no. 2, p. 021906, 2005.
 C. Bandt and B. Pompe, “Permutation entropy: a natural complexity measure for time series,” Physical review letters, vol. 88, no. 17, p. 174102, 2002.
 Z. Zhang, X. Guo, and Y. Lin, “Trust management method of d2d communication based on rf fingerprint identification,” IEEE Access, vol. 6, pp. 66 082–66 087, 2018.
 C. J. Burges, “A tutorial on support vector machine for pattern recognition,” Data mining and knowledge discovery, vol. 2, no. 2, pp. 955–974, 1998.
 C.-W. Hsu, C.-C. Chang, C.-J. Lin et al., “A practical guide to support vector classification,” 2003.