Date of Award

3-22-2019

Document Type

Thesis

Degree Name

Master of Science in Computer Engineering

Department

Department of Electrical and Computer Engineering

First Advisor

J. Addison Betances, PhD

Abstract

Low-Rate Wireless Personal Area Network(s) (LR-WPAN) usage has increased as more consumers embrace Internet of Things (IoT) devices. ZigBee Physical Layer (PHY) is based on the Institute of Electrical and Electronics Engineers (IEEE) 802.15.4 specification designed to provide a low-cost, low-power, and low-complexity solution for Wireless Sensor Network(s) (WSN). The standard’s extended battery life and reliability makes ZigBee WSN a popular choice for home automation, transportation, traffic management, Industrial Control Systems (ICS), and cyber-physical systems. As robust and versatile as the standard is, ZigBee remains vulnerable to a myriad of common network attacks. Previous research involving Radio Frequency-Distinct Native Attribute (RF-DNA) Fingerprinting and device discrimination has shown that bit-level WSN security can be augmented with PHY-based features. The objective of this research was to develop and implement an Radio Frequency (RF) air monitor system that classifies devices in Near Real-Time (NRT). The performance of the NRT air monitor is contrasted against previous research that utilized MATLAB-based Fingerprinting post-processing RF-DNA. The RF air monitor demonstration included collection of IEEE 802.15.4 bursts from Nd = 10 RZUSBsticks to assess NRT performance and effectiveness. The first set of experiments examined how well the air monitor recovered IEEE 802.15.4 data packets while fingerprinting and discriminating ZigBee devices under two distinct workloads. The second set of experiments compared predictive post-processed MATLAB RF-DNA Multiple Discriminant Analysis/Maximum Likelihood (MDA/ML) models Average Percent Correct Classification (%C ) against the air monitor’s observed operational %C for each RZUSBstick. The air monitor achieved an Overall Accurate Packet Reconstruction Percent (%R) GTOET 97.92% while correctly fingerprinting an Overall Fingerprinted Percent (%F ) GTOET 97.48% of the transmitted IEEE 802.15.4 data packets during the trials. The air monitor achieved an overall operational %C _ 96.93% at a collected Signal-to-Noise Ratio (SNR) NEARLY 33.571 dB, classified each RZUSBstick within 0.45 msec _ TMDA _ 1.5 msec after detection, and %C Deviation (%C_) = 2.71% from the collected post-processed MDA/ML model. The results support that an RF air monitor is feasible, can be effective, and will accurately operate within predictive post-processed MATLAB model estimations.

AFIT Designator

AFIT-ENG-MS-19-M-021

DTIC Accession Number

AD1074902

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