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Deep-Learning-Based Physical Layer Authentication for Industrial Wireless Sensor Networks
In this paper, a deep learning (DL)-based physical (PHY) layer authentication framework is proposed to enhance the security of industrial wireless sensor networks (IWSNs). Three algorithms, the deep neural network (DNN)-based sensor nodes’ authentication method, the convolutional neural network (CNN...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6603790/ https://www.ncbi.nlm.nih.gov/pubmed/31142016 http://dx.doi.org/10.3390/s19112440 |
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author | Liao, Run-Fa Wen, Hong Wu, Jinsong Pan, Fei Xu, Aidong Jiang, Yixin Xie, Feiyi Cao, Minggui |
author_facet | Liao, Run-Fa Wen, Hong Wu, Jinsong Pan, Fei Xu, Aidong Jiang, Yixin Xie, Feiyi Cao, Minggui |
author_sort | Liao, Run-Fa |
collection | PubMed |
description | In this paper, a deep learning (DL)-based physical (PHY) layer authentication framework is proposed to enhance the security of industrial wireless sensor networks (IWSNs). Three algorithms, the deep neural network (DNN)-based sensor nodes’ authentication method, the convolutional neural network (CNN)-based sensor nodes’ authentication method, and the convolution preprocessing neural network (CPNN)-based sensor nodes’ authentication method, have been adopted to implement the PHY-layer authentication in IWSNs. Among them, the improved CPNN-based algorithm requires few computing resources and has extremely low latency, which enable a lightweight multi-node PHY-layer authentication. The adaptive moment estimation (Adam) accelerated gradient algorithm and minibatch skill are used to accelerate the training of the neural networks. Simulations are performed to evaluate the performance of each algorithm and a brief analysis of the application scenarios for each algorithm is discussed. Moreover, the experiments have been performed with universal software radio peripherals (USRPs) to evaluate the authentication performance of the proposed algorithms. Due to the trainings being performed on the edge sides, the proposed method can implement a lightweight authentication for the sensor nodes under the edge computing (EC) system in IWSNs. |
format | Online Article Text |
id | pubmed-6603790 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-66037902019-07-17 Deep-Learning-Based Physical Layer Authentication for Industrial Wireless Sensor Networks Liao, Run-Fa Wen, Hong Wu, Jinsong Pan, Fei Xu, Aidong Jiang, Yixin Xie, Feiyi Cao, Minggui Sensors (Basel) Article In this paper, a deep learning (DL)-based physical (PHY) layer authentication framework is proposed to enhance the security of industrial wireless sensor networks (IWSNs). Three algorithms, the deep neural network (DNN)-based sensor nodes’ authentication method, the convolutional neural network (CNN)-based sensor nodes’ authentication method, and the convolution preprocessing neural network (CPNN)-based sensor nodes’ authentication method, have been adopted to implement the PHY-layer authentication in IWSNs. Among them, the improved CPNN-based algorithm requires few computing resources and has extremely low latency, which enable a lightweight multi-node PHY-layer authentication. The adaptive moment estimation (Adam) accelerated gradient algorithm and minibatch skill are used to accelerate the training of the neural networks. Simulations are performed to evaluate the performance of each algorithm and a brief analysis of the application scenarios for each algorithm is discussed. Moreover, the experiments have been performed with universal software radio peripherals (USRPs) to evaluate the authentication performance of the proposed algorithms. Due to the trainings being performed on the edge sides, the proposed method can implement a lightweight authentication for the sensor nodes under the edge computing (EC) system in IWSNs. MDPI 2019-05-28 /pmc/articles/PMC6603790/ /pubmed/31142016 http://dx.doi.org/10.3390/s19112440 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Liao, Run-Fa Wen, Hong Wu, Jinsong Pan, Fei Xu, Aidong Jiang, Yixin Xie, Feiyi Cao, Minggui Deep-Learning-Based Physical Layer Authentication for Industrial Wireless Sensor Networks |
title | Deep-Learning-Based Physical Layer Authentication for Industrial Wireless Sensor Networks |
title_full | Deep-Learning-Based Physical Layer Authentication for Industrial Wireless Sensor Networks |
title_fullStr | Deep-Learning-Based Physical Layer Authentication for Industrial Wireless Sensor Networks |
title_full_unstemmed | Deep-Learning-Based Physical Layer Authentication for Industrial Wireless Sensor Networks |
title_short | Deep-Learning-Based Physical Layer Authentication for Industrial Wireless Sensor Networks |
title_sort | deep-learning-based physical layer authentication for industrial wireless sensor networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6603790/ https://www.ncbi.nlm.nih.gov/pubmed/31142016 http://dx.doi.org/10.3390/s19112440 |
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