Near Real-Time Zigbee Device Discrimination Using CB-DNA Features
Abstract
Currently, LR-WPAN based on the IEEE 802.15.4 standard are at risk due to open-source tools which allow bad actors to exploit unauthorized network access through various cyber attacks by falsifying bit-level credentials. This research investigates implementing a RFair monitor to perform 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 CB-DNA fingerprints. Through the use of CB-DNA fingerprints, 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 percent C_{tst} = 99:24 percent during the testing of N_{cls} = 5 like-model Blade RF Software Defined Radios transmitting Zigbee protocol bursts. Additionally, to evaluate the NRT capability of the air monitor, a statistical analysis of N_{timing} = 1000 Zigbee bursts determined the worst-case average runtime from burst detection to classification. The analysis concluded that the runtime was t_{runtime} approximately 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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 26, 2020
- Accession Number
- AD1102972
Entities
People
- Yousuke Z. Matsui
Organizations
- Air Force Institute of Technology