Using Support Vector Machines for Passive Steady State RF Fingerprinting

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 and provide a detailed misclassification analysis that offers encouraging ideas for further improvements to the technique.