AdaBoost

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.

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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