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Feature Selection and Classifier Development for Radio Frequency Device Identification

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Technical Report,01 Jul 2011,24 Dec 2015

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Air Force Institute of Technology WPAFB United States

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The proliferation of simple and low-cost devices, such as IEEE 802.15.4 ZigBee and Z-Wave, in Critical Infrastructure CI increases security concerns. Radio Frequency Distinct Native Attribute RF-DNA Fingerprinting facilitates biometric-like identification of electronic devices emissions from variances in device hardware. Developing reliable classifier models using RF-DNA fingerprints is thus important for device discrimination to enable reliable Device Classification a one-to-many looks most like assessment and Device ID Verification a one-to-one looks how much like assessment. AFITs prior RF-DNA work focused on Multiple Discriminant AnalysisMaximum Likelihood MDAML and Generalized Relevance Learning Vector Quantized Improved GRLVQI classifiers. This work 1 introduces a new GRLVQI-Distance GRLVQI-D classifier that extends prior GRLVQI work by supporting alternative distance measures, 2 formalizes a framework for selecting competing distance measures for GRLVQI-D, 3 introducing response surface methods for optimizing GRLVQI and GRLVQI-D algorithm settings, 4 develops an MDA-based Loadings Fusion MLF Dimensional Reduction Analysis DRA method for improved classifier-based feature selection, 5 introduces the F-test as a DRA method for RF-DNA fingerprints, 6 provides a phenomenological understanding of test statistics and p-values, with KS-test and F-test statistic values being superior to p-values for DRA, and 7 introduces quantitative dimensionality assessment methods for DRA subset selection.

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