Abstract
Radio Frequency (RF) fingerprinting is a security mechanism inspired by biological fingerprint identification systems. RF fingerprinting is proposed as a means of providing an additional layer of security for wireless devices. RF fingerprinting classification is performed by selecting an “unknown” signal from the pool, generating its RF fingerprint, and using a classifier to correlate the received RF fingerprint with each profile RF fingerprint stored in the database. Unlike a human biological fingerprint, RF fingerprint of a wireless device changes with the received Signal to Noise Ratio (SNR) and varies due to mobility of the transmitter/receiver and environment. The variations in the features of RF fingerprints affect the classification results of the RF fingerprinting. This chapter evaluates the performance of the KNN and neural network classification for varying SNR. Performance analysis is performed for three scenarios that correspond to the situation, when either transmitter or receiver is mobile, and SNR changes from low to high or vice versa.
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