Abstract
Passive steady state RF Fingerprinting has recently been proposed as a promising new method for identifying a radio transmitter. In essence, the algorithm detects the differences imbued on a signal as it passes through the analogue stages of a transmit chain. In this paper we improve the algorithms performance and scalability by proposing a new more sophisticated classification engine. The classifier engine is based on a one-against-one multi class support vector machine. We measure the improved system’s performance in the largest, most representative case study of its kind - 73,000 measurements across 41 models of UMTS user equipment (UE). We achieve 94.2% classification accuracy. In addition we provide detailed misclassification analysis and outline how the analysis can be used to considerably further improve overall classification accuracy.
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Reference
R.M. Gerdes, T.E. Daniels, M. Mina and S.F. Russell, “ Device Identification via Analog Signal Fingerprinting: A Matched Filter Approach”, ISOC Network and Distributed, System Security Symposium, February 2006.
L.T.W. Ho and H. Claussen, “Effects of User-Deployed, Co-Channel Femtocells on the Call Drop Probability in a Residential Scenario”, IEEE International Symposium on Personal, Indoor and Mobile Communications, September 2007.
I.O. Kennedy, P.Scanlon and M. Buddhikiot, “Passive Steady State RF Fingerprinting: A Cognitive Technique for Scalable Deployment of Co-channel Femto Cell Underlays”, Proceedings IEEE Conference on Dynamic Spectrum Access Networks, October 2008.
J. Hall, M. Barbeau and E. Kranakis, “Detecting rogue devices in Bluetooth networks using Radio Frequency Fingerprinting”, Proceedings of the International Conference on Communications and Computer Networks, October 2006.
D. Shaw and W. Kinsner, “Multifractal Modelling of Radio Transmitter Transients for Classification”, Proceedings Conference on Communications, Power and Computing, pp. 306–312, May 1997.
H. Tekbas, N. Serinken and O. Ureten, “An experimental performance evaluation of a novel radio-transmitter identification system under diverse environmental conditions”, Canadian Journal Computer Engineering, 2004.
O. Ureten and N. Serinken, “Bayesian detection of transmitter turn-on transients”, Proceedings NSIP99, pp. 830–834, June 1999.
O. Ureten and N. Serinken, “Detections of radio transmitter turn-on transients”, Electronic Letters, vol. 35, pp. 1996–1997, November 1999.
K. Ellis and N. Serinken. Characteristics of radio transmitter fingerprints. Journal of Radio Science, pp. 585–597, 2001.
C.J. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition”, Knowledge Discovery and Data Mining, vol. 2, pp. 121 – 167, 1998.
B. ScholKopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, 2002.
S. Knerr, L. Personnaz and G. Dreyfus, “Single-layer Learning Revisited: a Stepwise Procedure for Building and Training a Neural Network”, Neurocomputing: Algorithms, Architectures and Applications, 1990.
C.-W. Hsu and C.-J. Lin, “A Comparison of Methods for Multiclass Support Vector Machines”, IEEE Transactions on Neural Networks, vol. 13, pp. 412–425, 2002.
J. Milgram, M. Cheriet and R. Sabourin, “‘One Against One’ or ‘One Against All’: Which One is Better for Handwriting Recognition with SVMs?”, Tenth International Workshop on Frontiers in Handwriting Recognition, 2006.
J. Platt, “Probabilistic outputs for support vector machines and comparison to regularized likelihood methods”, Advances in Large Margin Classifiers , pp. 61–74, 2000.
T. Luke, A.M. Gardiner, J. Kvall, “Global Wireless Semiconductor Update”, Lehman Brothers Equity Research Note, April 29th 2008.
Acknowledgments
The authors wish to acknowledge Henry Liu for the measurements used in this work. This work was in part supported by the IDA Ireland.
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Zamora, G.O., Bergin, S., Kennedy, I.O. (2010). Using Support Vector Machines for Passive Steady State RF Fingerprinting. In: Sobh, T., Elleithy, K., Mahmood, A. (eds) Novel Algorithms and Techniques in Telecommunications and Networking. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-3662-9_31
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DOI: https://doi.org/10.1007/978-90-481-3662-9_31
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