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. 2019 May 28;19(11):2440.
doi: 10.3390/s19112440.

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

Run-Fa Liao  1 , Hong Wen  2 , Jinsong Wu  3 , Fei Pan  4 , Aidong Xu  5 , Yixin Jiang  6 , Feiyi Xie  7 , Minggui Cao  8
Affiliations

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

Run-Fa Liao et al. Sensors (Basel). .

Abstract

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.

Keywords: PHY-layer; WSN; industrial; light-weight authentication; neural network.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The deep neural network.
Figure 2
Figure 2
The convolutional neural network. CSI, channel state information.
Figure 3
Figure 3
The system model of DL-based PHY-layer authentication in IWSNs.
Figure 4
Figure 4
DL-based PHY-layer authentication flow chart.
Figure 5
Figure 5
The authentication performance with different sensor nodes. (a) The cost value under different numbers of sensor nodes with the DNN-based method; (b) The authentication rate under different numbers of sensor nodes with the DNN-based method.
Figure 6
Figure 6
The authentication performance with different numbers of hidden layers. (a) The authentication rate of different numbers of hidden layers; (b) The authentication rate of different numbers of hidden layers after training was stabilized.
Figure 7
Figure 7
The authentication performance with different algorithms. (a) The authentication rate of different algorithms under different numbers of sensor nodes; (b) The time in the training phase of different algorithms under different numbers of sensor nodes.
Figure 8
Figure 8
The network topology.
Figure 9
Figure 9
The location of the wireless sensor nodes in the practical industrial scenario.
Figure 10
Figure 10
The authentication rate with USRPs.
See this image and copyright information in PMC

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