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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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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