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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