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

doi:10.3390/s19112440

Cited by 72 publications

(54 citation statements)

References 30 publications

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“…Authors have used the received CSI and known locations of wireless transmitters as input to machine learning systems that are then used to accurately determine the position of the transmitters at a later time [33], [34], [35]. Additionally, there has been physical-layer authentication research based on CSI using spatial information and machine learning [36], [37], [38], [39]. Pan et al showed that static environments, rich fading, and antennas separated by greater than a half wavelength improved authentication performance [39].…”

Section: Related Workmentioning

confidence: 99%

Channel Prediction and Transmitter Authentication With Adversarially-Trained Recurrent Neural Networks

Germain

Kragh

2021

IEEE Open J. Commun. Soc.

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As wireless communications and interconnected networks become ubiquitous and relied upon, they must also remain secure. Advanced communication systems that use techniques to improve data throughput and minimize latency lend themselves to physicallayer authentication. The stochastic and dynamic nature of the wireless mobile channel provides features that can be extracted through deep learning. We propose a novel method to authenticate transmitters at the physical layer by leveraging channel state information to predict future channel impulse responses. Specifically, we compare the use of recurrent neural networks (RNNs) using long-short term memory (LSTM) and gated recurrent unit (GRU) cells with variations of a conditional generative adversarial network (CGAN) to authenticate transmitters in a mobile environment. Our evaluation shows that standalone RNNs using LSTM and GRU cells are adept at predicting future channel responses, however a CGAN-trained discriminator using GRU cells is able to match the authentication accuracy of a standalone network without using a predefined channel prediction error threshold. Using a discriminator trained by a CGAN with binary cross entropy loss in the discriminator and mean squared error loss in the generator, the neural network was able to authenticate at a 98.5% rate.

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Section: Related Workmentioning

confidence: 99%

Channel Prediction and Transmitter Authentication With Adversarially-Trained Recurrent Neural Networks

Germain

Kragh

2021

IEEE Open J. Commun. Soc.

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“…In paper [142], in order to improve the secure operation of industrial wireless sensor networks (IWSNs), a physical layer authentication based on deep learning is presented. Three different authentication methods for sensor nodes, more specifically the deep neural network (DNN), the convolutional neural network (CNN) and convolution pre-processing neural network (CPNN) have been used to deploy the PHY-layer authentication in IWSNs.…”

Section: A Review Of Articles For Special Issue On Green Energy-ementioning

confidence: 99%

Greener, Energy-Efficient and Sustainable Networks: State-Of-The-Art and New Trends

Lörincz

Capone

2019

Sensors

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Although information and communications technologies (ICTs) have the potential of enabling powerful social, economic and environmental benefits, ICT systems give a non-negligible contribution to world electricity consumption and carbon dioxide (CO2) footprint. This contribution will sustain since the increased demand for user′s connectivity and an explosion of traffic volumes necessitate continuous expansion of current ICTs services and deployment of new infrastructures and technologies which must ensure the expected user experiences and performance. In this paper, analyses of costs for the global annual energy consumption of telecommunication networks, estimation of ICT sector CO2 footprint contribution and predictions of energy consumption of all connected user-related devices and equipment in the period 2011–2030 are presented. Since presented estimations of network energy consumption trends for main communication sectors by 2030 shows that highest contribution to global energy consumption will come from wireless access networks and data centres (DCs), the rest of the paper analyses technologies and concepts which can contribute to the energy-efficiency improvements of these two sectors. More specifically, different paradigms for wireless access networks such as millimetre-wave communications, Long-Term Evolution in unlicensed spectrum, ultra-dense heterogeneous networks, device-to-device communications and massive multiple-input multiple-output communications have been analysed as possible technologies for improvement of wireless networks energy efficiency. Additionally, approaches related to the DC resource management, DCs power management, green DC monitoring and thermal management in DCs have been discussed as promising approaches to improvement of DC power usage efficiency. For each of analysed technologies, future research challenges and open issues have been summarised and discussed. Lastly, an overview of the accepted papers in the Special Issue dedicated to the green, energy-efficient and sustainable networks is presented.

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“…Reference [17] proposes a threshold-free physical layer authentication scheme based on machine learning, which improves the detection accuracy without increasing the amount of calculation. To detect the spoofers, [18] presents several adaptive moment estimation algorithms based on deep neural network for lightweight authentication, which can accelerate the training of the authentication model and guarantee extremely low latency. Similarly, neural networks proposed in [19] are used to optimize the encoding and decoding functions, and to learn the trade-off between reliable communication and information secrecy to distinguish eavesdroppers.…”

Section: Related Workmentioning

confidence: 99%

Artificial Intelligence-Based Security Authentication: Applications in Wireless Multimedia Networks

Qiu

Sun

2019

IEEE Access

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Security provisioning has become a key issue in wireless multimedia networks due to their vital roles in supporting various services. Conventional security solutions have deficiencies in computing efficiency and addressing emerging security challenges. In addition, traditional upper-layer authentication ignores the protection of the physical layer, leading to severe privacy data leakage. In this paper, we envision a new deep learning (DL)-enabled security authentication scheme to overcome these issues, while implementing Blind Feature Learning (BFL) and Lightweight Physical Layer Authentication (LPLA). Specifically, an intelligent authentication method is developed by exploring neural networks at the data collection unit to learn the characteristics of data. Then we propose a holistic authentication scheme based on machine learning to identify malicious multimedia devices. Experimental analysis verifies that the proposed scheme can guarantee the privacy of wireless multimedia sensors and achieve lightweight authentication. Performance results indicate that artificial intelligence-enabled authentication scheme improves the overall security of multimedia networks. INDEX TERMS Multimedia network, machine learning, security, neural network.

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

Cited by 72 publications

References 30 publications

Channel Prediction and Transmitter Authentication With Adversarially-Trained Recurrent Neural Networks

Channel Prediction and Transmitter Authentication With Adversarially-Trained Recurrent Neural Networks

Greener, Energy-Efficient and Sustainable Networks: State-Of-The-Art and New Trends

Artificial Intelligence-Based Security Authentication: Applications in Wireless Multimedia Networks

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