PROJECT TITLE :

Improving ZigBee Device Network Authentication Using Ensemble Decision Tree Classifiers With Radio Frequency Distinct Native Attribute Fingerprinting

ABSTRACT:

The popularity of ZigBee devices continues to grow in home automation, transportation, traffic management, and Industrial Management System (ICS) applications given their low-price and low-power. However, the decentralized design of ZigBee ad-hoc networks creates distinctive 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 employing a Fisher-based Multiple Discriminant Analysis and Most Probability (MDA-ML) classification method to distinguish between devices in low Signal-to-Noise Ratio (SNR) environments. But, MDA-ML performance inherently degrades when RF-DNA options do not satisfy Gaussian normality conditions, that usually happens in real-world scenarios where radio frequency (RF) multipath and interference from alternative devices is gift. We introduce non-parametric Random Forest (RndF) and Multi-Category 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, specifically a pre-classification Kolmogorov-Smirnoff Take a look at (KS-Take a look at), a post-classification RndF feature relevance ranking, and a GRLVQI feature relevance ranking. Using the ensemble strategies, an $rm SNR=18.0$ dB improvement over MDA-ML processing is realized at an arbitrary correct classification rate $(hbox%C)$ benchmark of $hbox%C=90hbox%$ ; for all $rm SNRin [0, thirty]$ dB consid- red, $hbox%C$ improvement over MDA-ML ranged from 9p.c to twenty fourp.c. Relative to GRLVQI processing, ensemble ways once more provided improvement for all SNR, with a best improvement of $hbox%C=10hbox%$ achieved at rock bottom tested $rm SNR=0.0$ dB. Network penetration, measured using rogue ZigBee devices, show that at the $rm SNR=12.zero$ dB $(hbox%C=90hbox%)$ the ensemble methods properly reject 31 of thirty six rogue access makes an attempt based on Receiver Operating Characteristic (ROC) curve analysis and an arbitrary Rogue Settle for Rate of $rm RAR < 10hbox%$. This performance is healthier than MDA-ML, and GRLVQI that rejected twenty five/thirty six, and twenty eight/thirty six rogue access makes an attempt respectively. The key benefit of ensemble method processing is improved rogue rejection in noisier environments; gains of 6.0 dB, and 18.zero dB are realized over GRLVQI, and MDA-ML, respectively. Collectively considering the demonstrated $hbox%C$ and rogue rejection capability, the use of ensemble strategies improves ZigBee network authentication, and enhances anti-spoofing protection afforded by RF-DNA fingerprinting.


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