The Internet of Things (IoT) is a collection of Internet connected devices capable of interacting with the physical world and computer systems. It is estimated that IoT will consist of more than seventy five billion devices by the year 2025. In addition to the sheer numbers, the need for IoT security is exacerbated by the fact that many of the edge devices employ weak to no encryption of the communication link. It has been estimated that almost 70% of IoT devices use no form of encryption. Previous research has suggested the use of Specific Emitter Identification (SEI), a physical layer technique, as a means of augmenting bit-level security mechanisms such as encryption. Radio Frequency-Distinct Native Attributes (RF-DNA) fingerprinting is an SEI technique that has demonstrated success in discriminating radios operating within a noise only channel. This work extends RF-DNA fingerprinting to the discrimination of radios operating under Rayleigh fading conditions through the use of a Nelder-Mead (N-M) simplex-based channel estimator. The N-M estimator estimates the multipath channel directly from the received waveform; thus, eliminating the need for demodulation that is required when using constellation-based estimators. N-M estimator proves superior to three alternative waveform-based estimation approaches under increasing fading paths/reflections and decreasing Signal-to-Noise Ratio (SNR). Radio discrimination performance is maximized through the assessment of: (i) RF-DNA fingerprints generated from the magnitude versus phase representation of the Gabor transform's coefficients, (ii) a statistic-based classifier versus a neural network-based classifier, and (iii) the size of patch used to subdivide the Gabor-based time-frequency response prior to calculation of the RF-DNA fingerprint features. The resulting RF-DNA fingerprinting process achieves an average percent correct classification of 92.3% or greater for Rayleigh fading channels consisting of: two, three, or five reflections/paths at SNR≥15 dB.

The Internet of Things is a collection of Internet connected devices capable of interacting with the physical world and computer-based systems and is estimated to consist of 20 to 50 billion devices by the year 2020. Due to these numbers and the fact that 70% of the edge devices have no or poor encryption, there is a need for mechanisms by which to secure these devices and associated networks. Specific Emitter Identification (SEI) is one proposed mechanism for IoT security; however, performance within multipath environments has received little attention. This work presents the integration of a novel, Nelder-Mead (N-M)-based channel estimator within the Radio Frequency-Distinct Native Attributes (RF-DNA) fingerprinting process to facilitate serial number discrimination when IoT devices are operating within a multipath environment and degrading Signal-to-Noise Ratio. Percent correct classification performance is used to assess the developed RF-DNA fingerprinting process. Two additional SEI approaches are assessed to facilitate comparative analysis of the serial number discrimination of four IEEE 802.11a Wi-Fi radios using a Rayleigh fading channel. Relative to the Adaptive Compensator (A-C) and N-M SEI approaches, RF-DNA fingerprinting provides the best means for achieving reliable (better than 95%) identification of all radios within a L=2 Rayleigh fading channel at SNR ≥ 21 dB.