Date of Award
3-2024
Document Type
Thesis
Degree Name
Master of Science in Computer Science
Department
Department of Electrical and Computer Engineering
First Advisor
Jose A. Gutierrez del Arroyo, PhD
Abstract
Radio Frequency Fingerprinting (RFF) is the process of creating discerning signatures of emitted radio signals, most often with the goal of identifying specific devices again in the future. The security benefits of this task are intended to build upon current software-based authentication by making use of multi-factor authentication (MFA), but the related task of being able to reject unwanted emitters is limited. This paper presents a Siamese network trained on two different extracted fingerprints of raw Wi-Fi signals, along with a verifier to perform classification and rogue device detection. It was found that fingerprints using the Distortion Reconstruction (DR) technique outperform the popular Time-Domain Distinct Native Attributes (TD-DNA) method with respective classification accuracies of 99.15% and 85.88% when trained on 190 classes, and using a modified triplet loss with the Siamese network could create embeddings of the fingerprints capable of comparable classification while being able to reject rogue devices better than a straightforward classifier, with respective rejection rates of 98.12% and 86.88%, and while using one-third fewer parameters.
AFIT Designator
AFIT-ENG-MS-24-M-032
DTIC Accession Number
AD1318934
Recommended Citation
Wright, Mason, "Improving Rogue Radio Emitter Detection Using Siamese Networks" (2024). Theses and Dissertations. 7691.
https://scholar.afit.edu/etd/7691
Comments
A 12-month embargo was observed for posting this work on AFIT Scholar.
Distribution Statement A, Approved for Public Release. PA case number on file.