MDA/ML

Introduction of Random Forest Classifier to ZigBee Device Network Authentication Using RF-DNA Fingerprinting

ABSTRACT

The decentralized architecture of ZigBee ad-hoc networks creates unique security challenges to ensure only authentic devices are granted network access. Non-parametric Random Forest (RndF) and Multi-Class AdaBoost (MCA) ensemble classifiers were introduced with RF-Distinct Native Attribute (RF-DNA) fingerprinting to enhance device authentication performance. Correct classification (%C) performance is improved up to 24% over other classifiers, with 10% improvement at the lowest SNR = 0.0 dB. Network intrusion tests correctly rejected 31/36 rogue devices vs. 25/36 and 28/36 with previously used classifiers. The key benefit of ensemble method processing is improved rogue rejection in noisy environments–gains of up to Gs = 18.0 dB are realized over other classifiers. Collectively considering demonstrated %C and rogue rejection capability, the use of ensemble methods improves ZigBee network authentication and enhances anti-spoofing protection afforded by RF-DNA fingerprinting.

Published In

Volume 13, Issue 3

Journal of Information Warfare

The definitive publication for the best and latest research and analysis on information warfare, information operations, and cyber crime. Available in traditional hard copy or online.

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The definitive publication for the best and latest research and analysis on information warfare, information operations, and cyber crime. Available in traditional hard copy or online.

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