Date of Award
Master of Science in Electrical Engineering
Department of Electrical and Computer Engineering
J. Addison Betances, PhD
Currently, Low-Rate Wireless Personal Area Networks (LR-WPAN) based on the Institute of Electrical and Electronics Engineers (IEEE) 802.15.4 standard are at risk due to open-source tools which allow bad actors to exploit unauthorized network access through various cyberattacks by falsifying bit-level credentials. This research investigates implementing a Radio Frequency (RF) air monitor to perform Near RealTime (NRT) discrimination of Zigbee devices using the IEEE 802.15.4 standard. The air monitor employed a Multiple Discriminant Analysis/Euclidean Distance classifier to discriminate Zigbee devices based upon Constellation-Based Distinct Native Attribute (CB-DNA) fingerprints. Through the use of CB-DNA fingerprints, Physical Layer (PHY) characteristics unique to each Zigbee device strengthen the native bit-level authentication process for LR-WPAN networks. Overall, the developed RF air monitor achieved an Average Cross-Class Percent Correct Classification of %Ctst = 99:24% during the testing of Ncls = 5 like-model BladeRF Software Defined Radios transmitting Zigbee protocol bursts. Additionally, to evaluate the NRT capability of the air monitor, a statistical analysis of Ntiming = 1000 Zigbee bursts determined the worst-case average runtime from burst detection to classification. The analysis concluded that the runtime was truntime fi 269 mSec. Ultimately, this research found that PHY characteristics provide an additional method of authentication NRT to enhance the inherent network security for Zigbee applications from cyberattacks.
Matsui, Yousuke Z., "Near Real-Time Zigbee Device Discrimination Using CB-DNA Features" (2020). Theses and Dissertations. 3182.