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Open AccessArticle

Deep-Learning-Based Physical Layer Authentication for Industrial Wireless Sensor Networks

1
National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu 611731, China
2
School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China
3
Department of Electrical Engineering, Universidad de Chile, Santiago 8370451, Chile
4
EPRI, China Southern Power Grid Co., Ltd., Guangzhou 510080, China
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(11), 2440; https://doi.org/10.3390/s19112440
Received: 18 April 2019 / Revised: 18 May 2019 / Accepted: 27 May 2019 / Published: 28 May 2019
(This article belongs to the Special Issue Green, Energy-Efficient and Sustainable 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)-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. View Full-Text
Keywords: PHY-layer; light-weight authentication; neural network; WSN; industrial PHY-layer; light-weight authentication; neural network; WSN; industrial
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MDPI and ACS Style

Liao, R.-F.; Wen, H.; Wu, J.; Pan, F.; Xu, A.; Jiang, Y.; Xie, F.; Cao, M. Deep-Learning-Based Physical Layer Authentication for Industrial Wireless Sensor Networks. Sensors 2019, 19, 2440.

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