Securing ZigBee Commercial Communications Using Constellation Based Distinct Native Attribute Fingerprinting

This work provides development of Constellation Based DNA (CB-DNA) Fingerprinting for use in systems employing quadrature modulations and includes network protection demonstrations for ZigBee offset quadrature phase shift keying modulation. Results are based on 120 unique networks comprised of seven...

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Main Authors: Christopher M. Rondeau, J. Addison Betances, Michael A. Temple
Format: Article
Language:English
Published: Hindawi-Wiley 2018-01-01
Series:Security and Communication Networks
Online Access:http://dx.doi.org/10.1155/2018/1489347
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spelling doaj-8a8c72ef01f14d3b98a4a4c25507ea3c2020-11-24T23:34:34ZengHindawi-WileySecurity and Communication Networks1939-01141939-01222018-01-01201810.1155/2018/14893471489347Securing ZigBee Commercial Communications Using Constellation Based Distinct Native Attribute FingerprintingChristopher M. Rondeau0J. Addison Betances1Michael A. Temple2Department of Electrical and Computer Engineering, US Air Force Institute of Technology, Wright-Patterson AFB, Dayton, OH 45433, USADepartment of Electrical and Computer Engineering, US Air Force Institute of Technology, Wright-Patterson AFB, Dayton, OH 45433, USADepartment of Electrical and Computer Engineering, US Air Force Institute of Technology, Wright-Patterson AFB, Dayton, OH 45433, USAThis work provides development of Constellation Based DNA (CB-DNA) Fingerprinting for use in systems employing quadrature modulations and includes network protection demonstrations for ZigBee offset quadrature phase shift keying modulation. Results are based on 120 unique networks comprised of seven authorized ZigBee RZSUBSTICK devices, with three additional like-model devices serving as unauthorized rogue devices. Authorized network device fingerprints are used to train a Multiple Discriminant Analysis (MDA) classifier and Rogue Rejection Rate (RRR) estimated for 2520 attacks involving rogue devices presenting themselves as authorized devices. With MDA training thresholds set to achieve a True Verification Rate (TVR) of TVR = 95% for authorized network devices, the collective rogue device detection results for SNR ≥ 12 dB include average burst-by-burst RRR ≈ 94% across all 2520 attack scenarios with individual rogue device attack performance spanning 83.32% < RRR < 99.81%.http://dx.doi.org/10.1155/2018/1489347
collection DOAJ
language English
format Article
sources DOAJ
author Christopher M. Rondeau
J. Addison Betances
Michael A. Temple
spellingShingle Christopher M. Rondeau
J. Addison Betances
Michael A. Temple
Securing ZigBee Commercial Communications Using Constellation Based Distinct Native Attribute Fingerprinting
Security and Communication Networks
author_facet Christopher M. Rondeau
J. Addison Betances
Michael A. Temple
author_sort Christopher M. Rondeau
title Securing ZigBee Commercial Communications Using Constellation Based Distinct Native Attribute Fingerprinting
title_short Securing ZigBee Commercial Communications Using Constellation Based Distinct Native Attribute Fingerprinting
title_full Securing ZigBee Commercial Communications Using Constellation Based Distinct Native Attribute Fingerprinting
title_fullStr Securing ZigBee Commercial Communications Using Constellation Based Distinct Native Attribute Fingerprinting
title_full_unstemmed Securing ZigBee Commercial Communications Using Constellation Based Distinct Native Attribute Fingerprinting
title_sort securing zigbee commercial communications using constellation based distinct native attribute fingerprinting
publisher Hindawi-Wiley
series Security and Communication Networks
issn 1939-0114
1939-0122
publishDate 2018-01-01
description This work provides development of Constellation Based DNA (CB-DNA) Fingerprinting for use in systems employing quadrature modulations and includes network protection demonstrations for ZigBee offset quadrature phase shift keying modulation. Results are based on 120 unique networks comprised of seven authorized ZigBee RZSUBSTICK devices, with three additional like-model devices serving as unauthorized rogue devices. Authorized network device fingerprints are used to train a Multiple Discriminant Analysis (MDA) classifier and Rogue Rejection Rate (RRR) estimated for 2520 attacks involving rogue devices presenting themselves as authorized devices. With MDA training thresholds set to achieve a True Verification Rate (TVR) of TVR = 95% for authorized network devices, the collective rogue device detection results for SNR ≥ 12 dB include average burst-by-burst RRR ≈ 94% across all 2520 attack scenarios with individual rogue device attack performance spanning 83.32% < RRR < 99.81%.
url http://dx.doi.org/10.1155/2018/1489347
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