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Cyber-Physical Security with RF Fingerprint Classification through Distance Measure Extensions of Generalized Relevance Learning Vector Quantization
المؤلفون المشاركون
Bihl, Trevor J.
Paciencia, Todd J.
Bauer, Kenneth W.
Temple, Michael A.
المصدر
Security and Communication Networks
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-02-24
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
Radio frequency (RF) fingerprinting extracts fingerprint features from RF signals to protect against masquerade attacks by enabling reliable authentication of communication devices at the “serial number” level.
Facilitating the reliable authentication of communication devices are machine learning (ML) algorithms which find meaningful statistical differences between measured data.
The Generalized Relevance Learning Vector Quantization-Improved (GRLVQI) classifier is one ML algorithm which has shown efficacy for RF fingerprinting device discrimination.
GRLVQI extends the Learning Vector Quantization (LVQ) family of “winner take all” classifiers that develop prototype vectors (PVs) which represent data.
In LVQ algorithms, distances are computed between exemplars and PVs, and PVs are iteratively moved to accurately represent the data.
GRLVQI extends LVQ with a sigmoidal cost function, relevance learning, and PV update logic improvements.
However, both LVQ and GRLVQI are limited due to a reliance on squared Euclidean distance measures and a seemingly complex algorithm structure if changes are made to the underlying distance measure.
Herein, the authors (1) develop GRLVQI-D (distance), an extension of GRLVQI to consider alternative distance measures and (2) present the Cosine GRLVQI classifier using this framework.
To evaluate this framework, the authors consider experimentally collected Z-wave RF signals and develop RF fingerprints to identify devices.
Z-wave devices are low-cost, low-power communication technologies seen increasingly in critical infrastructure.
Both classification and verification, claimed identity, and performance comparisons are made with the new Cosine GRLVQI algorithm.
The results show more robust performance when using the Cosine GRLVQI algorithm when compared with four algorithms in the literature.
Additionally, the methodology used to create Cosine GRLVQI is generalizable to alternative measures.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Bihl, Trevor J.& Paciencia, Todd J.& Bauer, Kenneth W.& Temple, Michael A.. 2020. Cyber-Physical Security with RF Fingerprint Classification through Distance Measure Extensions of Generalized Relevance Learning Vector Quantization. Security and Communication Networks،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1208404
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Bihl, Trevor J.…[et al.]. Cyber-Physical Security with RF Fingerprint Classification through Distance Measure Extensions of Generalized Relevance Learning Vector Quantization. Security and Communication Networks No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1208404
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Bihl, Trevor J.& Paciencia, Todd J.& Bauer, Kenneth W.& Temple, Michael A.. Cyber-Physical Security with RF Fingerprint Classification through Distance Measure Extensions of Generalized Relevance Learning Vector Quantization. Security and Communication Networks. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1208404
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1208404
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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