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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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 |
work_keys_str_mv |
AT christophermrondeau securingzigbeecommercialcommunicationsusingconstellationbaseddistinctnativeattributefingerprinting AT jaddisonbetances securingzigbeecommercialcommunicationsusingconstellationbaseddistinctnativeattributefingerprinting AT michaelatemple securingzigbeecommercialcommunicationsusingconstellationbaseddistinctnativeattributefingerprinting |
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1725528752590422016 |