Publication:
Physical-Layer Authentication Using Channel State Information and Machine Learning

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Authors
St. Germain, Ken
Kragh, Frank
Subjects
Physical-layer security
authentication
MIMO
CSI
machine learning
generative adversarial network
Advisors
Date of Issue
2020
Date
Publisher
ArXiv
Language
Abstract
Strong authentication in an interconnected wireless environment continues to be an important, but sometimes elusive goal. Research in physical-layer authentication using channel features holds promise as a technique to improve network security for a variety of devices. We propose the use of machine learning and measured multiple-input multiple-output communications channel information to make a decision on whether or not to authenticate a particular device. Our approach uses received channel state information to train a neural network in an adversarial setting. These characteristics are then used to maintain authentication in subsequent communication sessions. This work analyzes the use of information from the wireless environment for the purpose of authentication and demonstrates the employment of a generative adversarial neural network trained with received channel data to authenticate a transmitting device without prior knowledge of receiver noise.
Type
Preprint
Description
Series/Report No
Department
Electrical and Computer Engineering (ECE)
Organization
Identifiers
NPS Report Number
Sponsors
Funder
Format
5 p.
Citation
Germain, Ken St, and Frank Kragh. "Physical-Layer Authentication Using Channel State Information and Machine Learning." arXiv preprint arXiv:2006.03695 (2020).
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
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