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Title: Physical layer authentication of electronic communication networks

Abstract

A network authentication system can be configured for sampling a plurality of signal samples from a device on a network, providing the plurality of signal samples to a first machine-learned model that is configured to determine a device fingerprint based at least in part on the plurality of signal samples, and providing the device fingerprint to a second machine-learned model that is configured to classify the device based at least in part on the device fingerprint.

Inventors:
; ;
Issue Date:
Research Org.:
Applied Engineering Concepts, Inc., Eldersburg, MD (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1998360
Patent Number(s):
11645538
Application Number:
17/177,513
Assignee:
Applied Engineering Concepts, Inc. (Eldersburg, MD)
DOE Contract Number:  
SC0019922
Resource Type:
Patent
Resource Relation:
Patent File Date: 02/17/2021
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Suski, William Charles, Card, Christopher Alan, and Few, Brian Richard. Physical layer authentication of electronic communication networks. United States: N. p., 2023. Web.
Suski, William Charles, Card, Christopher Alan, & Few, Brian Richard. Physical layer authentication of electronic communication networks. United States.
Suski, William Charles, Card, Christopher Alan, and Few, Brian Richard. Tue . "Physical layer authentication of electronic communication networks". United States. https://www.osti.gov/servlets/purl/1998360.
@article{osti_1998360,
title = {Physical layer authentication of electronic communication networks},
author = {Suski, William Charles and Card, Christopher Alan and Few, Brian Richard},
abstractNote = {A network authentication system can be configured for sampling a plurality of signal samples from a device on a network, providing the plurality of signal samples to a first machine-learned model that is configured to determine a device fingerprint based at least in part on the plurality of signal samples, and providing the device fingerprint to a second machine-learned model that is configured to classify the device based at least in part on the device fingerprint.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2023},
month = {5}
}

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patent-application, February 2023


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