Date of Award
3-2024
Document Type
Thesis
Degree Name
Master of Science in Cyber Operations
Department
Department of Electrical and Computer Engineering
First Advisor
Barry Mullins, PhD
Abstract
This research explores the application of AI methodologies in classifying RF signals within the IEEE 802.11 spectrum, focusing on differentiating Wi-Fi standards and router signatures. Utilizing a HackRF One Software Defined Radio (SDR) tuned to 2.4 GHz Channel 6, the research involved collecting raw samples from five different 802.11 routers, which are then transformed into constellation plots using a Python script. These plots served as the input for training a Convolutional Neural Network (CNN) model, aimed at classifying both Wi-Fi standards and router manufacturers. The model highlighted its ability to generalize to unfamiliar hardware. The methodologies and findings of this research, along with an analysis of the results, are presented.
AFIT Designator
AFIT-ENG-MS-24-M-017
Recommended Citation
Knight, Jeffrey A., "Machine Learning-Based Classification of Wi-Fi Standards Using RF Signature" (2024). Theses and Dissertations. 7683.
https://scholar.afit.edu/etd/7683
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.