36 research outputs found
Preprint: Using RF-DNA Fingerprints To Classify OFDM Transmitters Under Rayleigh Fading Conditions
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 the IoT will consist of approximately fifty billion devices by
the year 2020. 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 mechanism such as encryption. The work
presented here integrates a Nelder-Mead based approach for estimating the
Rayleigh fading channel coefficients prior to the SEI approach known as RF-DNA
fingerprinting. The performance of this estimator is assessed for degrading
signal-to-noise ratio and compared with least square and minimum mean squared
error channel estimators. Additionally, this work presents classification
results using RF-DNA fingerprints that were extracted from received signals
that have undergone Rayleigh fading channel correction using Minimum Mean
Squared Error (MMSE) equalization. This work also performs radio discrimination
using RF-DNA fingerprints generated from the normalized magnitude-squared and
phase response of Gabor coefficients as well as two classifiers. Discrimination
of four 802.11a Wi-Fi radios achieves an average percent correct classification
of 90% or better for signal-to-noise ratios of 18 and 21 dB or greater using a
Rayleigh fading channel comprised of two and five paths, respectively.Comment: 13 pages, 14 total figures/images, Currently under review by the IEEE
Transactions on Information Forensics and Securit
Classification of bandlimited fsk4 and fsk8 signals
This paper compares two types of classifiers applied to bandlimited FSK4 and FSK8 signals. The first classifier employs the decision-theoretic approach and the second classifier is a neural network structure. Key features are extracted using a zero crossing sampler. A novel decision tree is proposed and optimum threshold values are found for the decision theoretic approach. For the neural network, the optimum structure is found to be the smallest structure to give 100% overall success rate. The performance of the both classifiers has been evaluated by simulating bandlimited FSK4 and FSK8 signals corrupted by Gaussian noise. It is shown that the neural network outperforms the decision-theoretic approach particularly for SN