Date of Award
3-24-2016
Document Type
Thesis
Degree Name
Master of Science in Electrical Engineering
Department
Department of Electrical and Computer Engineering
First Advisor
Samuel Stone, PhD.
Abstract
The research presented here provides a comparison of classification, verification, and computational time for three techniques used to analyze Unintentional Radio- Frequency (RF) Emissions (URE) from semiconductor devices for the purposes of device discrimination and operation identification. URE from ten MSP430F5529 16-bit microcontrollers were analyzed using: 1) RF Distinct Native Attribute (RFDNA) fingerprints paired with Multiple Discriminant Analysis/Maximum Likelihood (MDA/ML) classification, 2) RF-DNA fingerprints paired with Generalized Relevance Learning Vector Quantized-Improved (GRLVQI) classification, and 3) Time Domain (TD) signals paired with matched filtering. These techniques were considered for potential applications to detect counterfeit/Trojan hardware infiltrating supply chains and to defend against cyber attacks by monitoring executed operations of embedded systems in critical Supervisory Control And Data Acquisition (SCADA) networks.
AFIT Designator
AFIT-ENG-MS-16-M-048
DTIC Accession Number
AD1053877
Recommended Citation
Stone, Barron D., "Comparison of Radio Frequency Distinct Native Attribute and Matched Filtering Techniques for Device Discrimination and Operation Identification" (2016). Theses and Dissertations. 323.
https://scholar.afit.edu/etd/323