A Relationship between Parameters of Two Representations of Discrete LTI ARMA System Model and the Corresponding Device Fingerprints

Abstract:

Device fingerprints has potential to identify the source of digital communication information. Although identifying transmitters with their fingerprints remains an arduous question, fusion identification with multiple device fingerprints is a feasible methodology. In this paper, a relationship between parameters of two representations of discrete-time linear time-invariant (LTI) autoregressive moving average (ARMA) system model is derived which has definite physical meaning. With the relationship, a novel linear device fingerprints is proposed which may enlarge the inter-class distance of the transmitters to be identified. The proposed device fingerprints can be used for fusion identification of network devices and the corresponding ARMA systems especially for that with slight differences.

• Periodical: Applied Mechanics and Materials (Volumes 416-417)
• Pages: 1267-1273
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.416-417.1267
• Online since: September 2013
• Authors: Hong Lin Yuan, Zhi Hua Bao, Yan Yan
• Keywords: ARMA System Identification, Device Fingerprints, Device Identification, Fusion Identification, Hardware Security, Transmitter Identification

References

[1] Simon, H. : Communication Systems 4th Edition. John Wiley & Sons, Inc, New York(2001).

[2] Shigenobu, M., etc. : An Artificial Fingerprint Device(AFD): A Study of Identification Number Applications Utilizing Characteristics Variation of Polycrystalline Silicon TFTs. IEEE Transactions on Electron Devices. Vol. 50(6), 1451-1458(2003).

DOI: 10.1109/ted.2002.808526

[3] R.M. Gerdes, T.E. Daniels, M. Mina, and S.F. Russell. Device Identification via Analog Signal Fingerprinting : A Matched Filter Approach. Annual Symposium on Network and Distributed System Security(NDSS'2006), San Diego, California USA, (2006).

[4] D.G. Lee, J. -W. Han, D.S. Park, and I.Y. Lee. Intelligent Pervasive Network Authentication: S/Key Based Device Authentication. Consumer Communications and Networking Conference, 2009. CCNC 2009, 6th IEEE, 2009. pp.1-5.

DOI: 10.1109/ccnc.2009.4784994

[5] RANDALL, W. K., MICHAEL, A. T., MICHAEL, J. M., Application of wavelet denoising to improve OFDM-based signal detection and classification. Security and Communication Networks, 2010, vol. 3, pp.71-82.

DOI: 10.1002/sec.115

[6] M. Hamdi, A. Meddeb-Makhlouf, and N. Boudriga. Multilayer Statistical Intrusion Detection in Wireless Networks. Eurasip Journal on Advances in Signal Processing, (2009).

DOI: 10.1155/2009/368589

[7] A. Candore, O. Kocabas, and F. Koushanfar. Robust stable radiometric fingerprinting for wireless devices. Hardware-Oriented Security and Trust, 2009, HOST'09. IEEE International Workshop on, 2009, pp.43-49.

DOI: 10.1109/hst.2009.5224969

[8] H.L. Yuan, Z.H. Bao, A.Q. Hu, Power ramped-up preamble RF fingerprints of wireless transmitters. Radioengineering. Vol. 20(3).

[9] Kenneth I. Talbot, Paul R. Duley, and Martin H. Hyatt. Specific Emitter Identification and Verification. Technology Review Journal. Spring/Summer 2003, Vol. 11(11), pp.113-133.

[10] HONGLIN, Y., AIQUN, H., Preamble-based detection of Wi-Fi transmitter RF fingerprints. Electronics Letters, 2010, vol. 46, pp.1165-1167.

DOI: 10.1049/el.2010.1220