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Relevant bibliographies by topics / Physical layer authentication

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Journal articles

Academic literature on the topic 'Physical layer authentication'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 March 2023

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Journal articles on the topic "Physical layer authentication"

1

Yu, Paul L., John S. Baras, and Brian M. Sadler. "Physical-Layer Authentication." IEEE Transactions on Information Forensics and Security 3, no. 1 (2008): 38–51. http://dx.doi.org/10.1109/tifs.2007.916273.

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2

Lavanya, D. L., R. Ramaprabha, and K. Gunaseelan. "Privacy Preserving Physical Layer Authentication Scheme for LBS based Wireless Networks." Defence Science Journal 71, no. 2 (2021): 241–47. http://dx.doi.org/10.14429/dsj.71.15355.

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With the fast development in services related to localisation, location-based service (LBS) gains more importance amongst all the mobile wireless services. To avail the service in the LBS system, information about the location and identity of the user has to be provided to the service provider. The service provider authenticates the user based on their identity and location before providing services. In general, sharing location information and preserving the user’s privacy is a highly challenging task in conventional authentication techniques. To resolve these challenges in authenticating the

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3

Chen, Songlin, Hong Wen, Jinsong Wu, et al. "Physical-Layer Channel Authentication for 5G via Machine Learning Algorithm." Wireless Communications and Mobile Computing 2018 (October 2, 2018): 1–10. http://dx.doi.org/10.1155/2018/6039878.

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By utilizing the radio channel information to detect spoofing attacks, channel based physical layer (PHY-layer) enhanced authentication can be exploited in light-weight securing 5G wireless communications. One major obstacle in the application of the PHY-layer authentication is its detection rate. In this paper, a novel authentication method is developed to detect spoofing attacks without a special test threshold while a trained model is used to determine whether the user is legal or illegal. Unlike the threshold test PHY-layer authentication method, the proposed AdaBoost based PHY-layer authe

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4

Shi, Zhi Yuan, Chang Zheng Zhang, Cai Dan Zhao, Lian Fen Huang, and Yi Feng Zhao. "One Solution to Physical-Layer Authentication in Wireless Communication System." Advanced Materials Research 791-793 (September 2013): 2071–75. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.2071.

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Authentication is the process where claims of identity are verified. Most mechanisms of authentication exist above the physical layer, though some exist at the physical layer often with an additional cost in bandwidth. This paper introduces a general analysis and design framework for authentication at the physical layer where the authentication information is transmitted synchronously with the data. By superimposing a carefully designed secret modulation (wavelet transform) on the waveforms, authentication is added to the signal without requiring additional bandwidth. Simulation results are gi

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5

Xie, Ning, and Changsheng Chen. "Slope Authentication at the Physical Layer." IEEE Transactions on Information Forensics and Security 13, no. 6 (2018): 1579–94. http://dx.doi.org/10.1109/tifs.2018.2797963.

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6

Chen, Yi, Hong Wen, Jinsong Wu, et al. "Clustering Based Physical-Layer Authentication in Edge Computing Systems with Asymmetric Resources." Sensors 19, no. 8 (2019): 1926. http://dx.doi.org/10.3390/s19081926.

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In this paper, we propose a clustering based physical-layer authentication scheme (CPAS) to overcome the drawback of traditional cipher-based authentication schemes that suffer from heavy costs and are limited by energy-constrained intelligent devices. CPAS is a novel cross-layer secure authentication approach for edge computing system with asymmetric resources. The CPAS scheme combines clustering and lightweight symmetric cipher with physical-layer channel state information to provide two-way authentication between terminals and edge devices. By taking advantage of temporal and spatial unique

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7

Jing, Tao, Hongyan Huang, Yue Wu, Qinghe Gao, Yan Huo, and Jiayu Sun. "Threshold-free multi-attributes physical layer authentication based on expectation–conditional maximization channel estimation in Internet of Things." International Journal of Distributed Sensor Networks 18, no. 7 (2022): 155013292211078. http://dx.doi.org/10.1177/15501329221107822.

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With the number of Internet of Things devices continually increasing, the endogenous security of Internet of Things communication systems is growingly critical. Physical layer authentication is a powerful means of resisting active attacks by exploiting the unique characteristics inherent in wireless signals and physical devices. Many existing physical layer authentication schemes usually assume physical layer attributes obey certain statistical distributions that are unknown to receivers. To overcome the uncertainty, machine learning–based authentication approaches have been employed to implem

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8

Qiu, Xiaoying, Xuan Sun, and Monson Hayes. "Enhanced Security Authentication Based on Convolutional-LSTM Networks." Sensors 21, no. 16 (2021): 5379. http://dx.doi.org/10.3390/s21165379.

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The performance of classical security authentication models can be severely affected by imperfect channel estimation as well as time-varying communication links. The commonly used approach of statistical decisions for the physical layer authenticator faces significant challenges in a dynamically changing, non-stationary environment. To address this problem, this paper introduces a deep learning-based authentication approach to learn and track the variations of channel characteristics, and thus improving the adaptability and convergence of the physical layer authentication. Specifically, an int

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9

Zhang, Xiaolong, Wei Wu, and Bin Zhou. "Secure Physical Layer Transmission and Authentication Mechanism Based on Compressed Sensing of Multiple Antenna Arrays." Journal of Sensors 2021 (November 10, 2021): 1–11. http://dx.doi.org/10.1155/2021/7022297.

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Large-scale antenna technology has become one of the most promising technologies in 5G because of its ability to effectively improve the spectral efficiency and energy efficiency of the system, as well as its better robustness. In this paper, a large amount of CSI (channel state information) data is characterized by feature mining and law analysis, and a large number of channel characteristics of the physical layer have the advantages of randomness and uniqueness, etc. From the perspective of improving the security of the authentication mechanism and reducing the computational complexity of th

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10

Liao, Run-Fa, Hong Wen, Jinsong Wu, et al. "Deep-Learning-Based Physical Layer Authentication for Industrial Wireless Sensor Networks." Sensors 19, no. 11 (2019): 2440. http://dx.doi.org/10.3390/s19112440.

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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

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