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Improving Zig Bee Device Network Authentication Using Ensemble Decision Tree Classifiers With Radio Frequency Distinct Native Attribute Fingerprinting
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DOI：10.1109/TR.2014.2372432.png)
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Abstract
En 中文
The popularity of ZigBee devices continues to grow in home automation, transportation, traffic management, and Industrial Control System (ICS) applications given their low-cost and low-power. However, the decentralized architecture of ZigBee ad-hoc networks creates unique security challenges for network intrusion detection and prevention. In the past, ZigBee device authentication reliability was enhanced by Radio Frequency-Distinct Native Attribute (RF-DNA) fingerprinting using a Fisher-based Multiple Discriminant Analysis and Maximum Likelihood (MDA-ML) classification process to distinguish between devices in low Signal-to-Noise Ratio (SNR) environments. However, MDA-ML performance inherently degrades when RF-DNA features do not satisfy Gaussian normality conditions, which often occurs in real-world scenarios where radio frequency (RF) multipath and interference from other devices is present. We introduce non-parametric Random Forest (RndF) and Multi-Class AdaBoost (MCA) ensemble classifiers into the RF-DNA fingerprinting arena, and demonstrate improved ZigBee device authentication. Results are compared with parametric MDA-ML and Generalized Relevance Learning Vector Quantization-Improved (GRLVQI) classifier results using identical input feature sets. Fingerprint dimensional reduction is examined using three methods, namely a pre-classification Kolmogorov-Smirnoff Test (KS-Test), a post-classification RndF feature relevance ranking, and a GRLVQI feature relevance ranking. Using the ensemble methods, an SNR = 18.0 dB improvement over MDA-ML processing is realized at an arbitrary correct classification rate (%C) benchmark of %C = 90%; for all SNR is an element of [0,30] dB considered, %C improvement over MDA-ML ranged from 9% to 24%. Relative to GRLVQI processing, ensemble methods again provided improvement for all SNR, with a best improvement of %C = 10% achieved at the lowest tested SNR = 0.0 dB. Network penetration, measured using rogue ZigBee devices, show that at the SNR = 12.0 dB (%C = 90%) the ensemble methods correctly reject 31 of 36 rogue access attempts based on Receiver Operating Characteristic (ROC) curve analysis and an arbitrary Rogue Accept Rate of RAR < 10%. This performance is better than MDA-ML, and GRLVQI which rejected 25/36, and 28/36 rogue access attempts respectively. The key benefit of ensemble method processing is improved rogue rejection in noisier environments; gains of 6.0 dB, and 18.0 dB are realized over GRLVQI, and MDA-ML, respectively. Collectively considering the 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.
Keywords:
Radio frequency-distinct native attribute finger-printing
ZigBee
multiple discriminant analysis and maximum likelihood
generalized relevance learning vector quantization-improved
random forest
AdaBoost
security
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