Start Date

12-4-2021 10:00 AM

End Date

12-4-2021 10:00 AM

Publisher

University of Tennessee at Chattanooga

Place of Publication

Chattanooga (Tenn.)

Abstract

It is estimated that the number of Internet of Things (IoT) devices will reach 75 billion in the next five years. Most of those currently and soon-to-be deployed devices lack sufficient security to protect themselves and their networks from attacks by malicious IoT devices masquerading as authorized devices in order to circumvent digital authentication approaches. This work presents a Physical (PHY) layer IoT authentication approach capable of addressing this critical security need through the use of feature-reduced, Radio Frequency-Distinct Native Attributes (RF-DNA) fingerprints and Support Vector Machines (SVM). This work successfully demonstrates (i) authorized Identity (ID) verification across three trials of six randomly chosen radios at signal-to-noise ratios greater than or equal to 6 dB and (ii) rejection of all rogue radio ID spoofing attacks at signal-to-noise ratios greater than or equal to 3 dB using RF-DNA fingerprints whose features are selected using the Relief-F algorithm.

Date

4-12-2021

Document Type

posters

Language

English

Rights

http://rightsstatements.org/vocab/InC/1.0/

License

http://creativecommons.org/licenses/by-nc-nd/4.0/

09296236.pdf (2025 kB)
Published work from which the poster is derived.

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Apr 12th, 10:00 AM Apr 12th, 10:00 AM

Radio Identity Verification-based IoT Security Using RF-DNA Fingerprints and SVM

It is estimated that the number of Internet of Things (IoT) devices will reach 75 billion in the next five years. Most of those currently and soon-to-be deployed devices lack sufficient security to protect themselves and their networks from attacks by malicious IoT devices masquerading as authorized devices in order to circumvent digital authentication approaches. This work presents a Physical (PHY) layer IoT authentication approach capable of addressing this critical security need through the use of feature-reduced, Radio Frequency-Distinct Native Attributes (RF-DNA) fingerprints and Support Vector Machines (SVM). This work successfully demonstrates (i) authorized Identity (ID) verification across three trials of six randomly chosen radios at signal-to-noise ratios greater than or equal to 6 dB and (ii) rejection of all rogue radio ID spoofing attacks at signal-to-noise ratios greater than or equal to 3 dB using RF-DNA fingerprints whose features are selected using the Relief-F algorithm.