Specific emitter identification (SEI) is the technique which identifies the individual emitter based on the RF fingerprint of signal. Most existing SEI techniques based on the transient RF fingerprint are sensitive to noise and need different variables for transient detection and RF fingerprint extraction. This paper proposes a novel SEI technique for the common digital modulation signals, which is robust to Gaussian noise and can avoid the problem that different variables are needed for transient detection and RF fingerprint extraction. This makes the technique more practical. The technique works based on the signal's energy trajectory acquired by the fourth order cumulants. A relative smoothness measure detector is used to detect the starting point and endpoint of the transient signal. The polynomial fitting coefficients of the energy trajectory and transient duration form the RF fingerprint. The principal component analysis (PCA) technique is used to reduce the feature vector's dimension, and a support vector machine (SVM) classifier is used for classification. The signals captured from eight mobile phones are used to test the performance of the technique, and the experimental results demonstrate that it has good performance even at low SNR levels.
1. Danev, B., H. Luecken, S. Capkun, and K. EI Defrawy, "Attacks on physical-layer identification," Proc. ACM Conf. on Wireless Network Security,, 89-98, 2010.
2. Shaw, D. and W. Kinsner, "Multifractal modeling of radio transmitter transients for classification," Proc. WESCANEX'97, 306-312, 1997.
3. Ureten, O. and N. Serinken, "Detection of radio transmitter turnon transients," Electronics Letters, Vol. 35, No. 23, 1996-1997, 1999. doi:10.1049/el:19991369
4. Ureten, O. and N. Serinken, "Bayesian detection of Wi-Fi transmitter RF fingerprints," Electronics Letters, Vol. 41, No. 6, 373-374, 2005. doi:10.1049/el:20057769
5. Hall, J., M. Barbeau, and E. Kranakis, "Detection of transient in radio frequency fingerprinting using signal phase," Proceedings of the 3rd IASTED Int. Conf. on Wireless and Optical Communications, 13-18, 2003.
6. Lee, T. W., "IndependComponent Analysis: Theory and Applications," Kluwer Academic Publishers, 1999.
7. Donelli, M., "A rescue radar system for the detection of victims trapped under rubble based on the independent component analysis algorithm," Progress In Electromagnetics Research M, Vol. 19, 173-181, 2011. doi:10.2528/PIERM11061206
8. Hall, J., M. Barbeau, and E. Kranakis, "Enhancing intrusion detection in wireless networks using radio frequency fingerprinting," Proceedings of the 3rd IASTED International Conference on Communications, Internet and Information Technology (CIIT), 201-206, 2004.
9. Hall, J., M. Barbeau, and E. Kranakis, "Detecting rogue devices in bluetooth networks using radio frequency fingerprinting," IASTED International Conference on Communications and Computer Networks , 108-113, 2006.
10. Ur Rehman, S., K. Sowerby, and C. Coghill, "RF fingerprint extraction from the energy envelope of an instantaneous transient signal," Australian Communications Theory Workshop (AusCTW), 90-95, 2012. doi:10.1109/AusCTW.2012.6164912
11. Bonne Rasmussen, K. and S. Capkun, "Implications of radio ¯ngerprinting on the security of sensor networks," Proceedings of the Third International Conference on Security and Privacy in Communications Networks and the Workshops, IEEE,, 331-340, 2007.
12. Afolabi, O., K. Kim, and A. Ahmad, "On secure spectrum sensing in cognitive radio networks using emitters electromagnetic signature," Proceedings of 18th Internatonal Conference on Computer Communications and Networks , 1-5, 2009.
13. Ellis, K. and N. Serinken, "Characteristics of radio transmitter fingerprints," Radio Science, Vol. 36, No. 4, 585-597, 2001. doi:10.1029/2000RS002345
14. Xu, J., H. Zhao and T. Liang, "Method of empirical mode decompositions in radio frequency fingerprint," 2010 International Conference on Microwave and Millimeter Wave Technology (ICMMT), 1275-1278, 2010.
15. Zhao, C., L. Huang, L. Hu, and Y. Yao, "Transient fingerprint feature extraction for WLAN cards based on polynomial fitting," The 6th International Conference on Computer Science & Education (ICCSE 2011), 1099-1102, 2011. doi:10.1109/ICCSE.2011.6028826
16. Klein, R. W., M. A. Temple, and M. J. Mendenhall, "Application of wavelet-based RF fingerprinting to enhance wireless network security," Journal of Communications and Networks, 544-555, 2009. doi:10.1109/JCN.2009.6388408
17. Wang, L. and Y. Ren, "Recognition of digital modulation signals based on high order cumulants and support vector machines," ISECS International Colloquium on Computing, Communication, Control, and Management, 271-274, 2009. doi:10.1109/CCCM.2009.5267733
18. Zhou, X., Y.Wu, and B. Yang, "Signal classification method based on support vector machine and high-order cumulants," Wireless Sensor Network, 48-52, 2010.
12. Swami, A. and B. M. Sadler, "Hierarchical digital modulation classification using cumulants," IEEE Transactions on Communications, Vol. 48, No. 3, 416-429, 2000. doi:10.1109/26.837045
20. Paige, C. and LSQR: An algorithm for sparse linear, "LSQR: An algorithm for sparse linear equations and sparse least squares," ACM Trans. Math. Software, Vol. 8, 43, 1982. doi:10.1145/355984.355989
21. Robert-Granie, C., J.-L. Foulley, E. Maza, and R. Rupp, "Statistical analysis of somatic cell scores via mixed model methodology for longitudinal data," Anim. Res, Vol. 53, 259-273, 2004. doi:10.1051/animres:2004016
22. Xu, S., B. Huang, L. Xu, and Z. Xu, "Radio transmitter classi¯cation using a new method of stray features analysis," combined with PCA Military Communications Conference (MILCOM 2007), 1-5, 2007. doi:10.1109/MILCOM.2007.4454838
23. Burges, C. J. C., "A tutorial on support vector machines for pattern recognition," Data Mining and Knowledge Discovery, 121-167, 1998. doi:10.1023/A:1009715923555
24. Bermani, E., A. Boni, A. Kerhet, and A. Massa, "Kernels evaluation of SVM-based estimators for inverse scattering problems," Progress In Electromagnetics Research, Vol. 53, 167-188, 2005. doi:10.2528/PIER04090801
25. Bermani, E., A. Boni, S. Caorsi, M. Donelli, and A. Massa, "A multi-source strategy based on a learning-by-examples technique for buried object detection," Progress In Electromagnetics Research, Vol. 48, 185-200, 2004. doi:10.2528/PIER03110701
26. Tekbas, O. H., O. Ureten, and N. Serinken, "Improvement of transmitter identification system for low SNR transients," Electronics Letters, Vol. 40, No. 3, 192-183, 2004. doi:10.1049/el:20040160