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Статті в журналах з теми "Physical layer authentication"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 9 березня 2023

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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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Анотація:

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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Анотація:

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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Анотація:

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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Анотація:

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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Анотація:

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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Анотація:

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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11

Ayyildiz, Cem, Ramazan Cetin, Zulfidin Khodzhaev, et al. "Physical layer authentication for extending battery life." Ad Hoc Networks 123 (December 2021): 102683. http://dx.doi.org/10.1016/j.adhoc.2021.102683.

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12

Yu, Paul L., Gunjan Verma, and Brian M. Sadler. "Wireless physical layer authentication via fingerprint embedding." IEEE Communications Magazine 53, no. 6 (2015): 48–53. http://dx.doi.org/10.1109/mcom.2015.7120016.

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13

Wu, Xiaofu, and Zhen Yang. "Physical-Layer Authentication for Multi-Carrier Transmission." IEEE Communications Letters 19, no. 1 (2015): 74–77. http://dx.doi.org/10.1109/lcomm.2014.2375191.

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14

Cobb, William E., Eric D. Laspe, Rusty O. Baldwin, Michael A. Temple, and Yong C. Kim. "Intrinsic Physical-Layer Authentication of Integrated Circuits." IEEE Transactions on Information Forensics and Security 7, no. 1 (2012): 14–24. http://dx.doi.org/10.1109/tifs.2011.2160170.

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15

LI, Min, Shaoquan JIANG, and Yongjian LIAO. "Physical-Layer Authentication via a Dynamic Scaling." Chinese Journal of Electronics 29, no. 4 (2020): 651–59. http://dx.doi.org/10.1049/cje.2020.05.009.

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16

Wang, Shaoyu, Kaizhi Huang, Xiaoming Xu, Xiaoyan Hu, Jing Yang, and Liang Jin. "Unconditional Authentication Based on Physical Layer Offered Chain Key in Wireless Communication." Entropy 24, no. 4 (2022): 488. http://dx.doi.org/10.3390/e24040488.

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Анотація:

Authentication is a critical issue in wireless communication due to the impersonation and substitution attacks from the vulnerable air interface launched by the malicious node. There are currently two kinds of authentication research in wireless communication. One is based on cryptography and relies on computational complexity, the other is based on physical layer fingerprint and can not protect data integrity well. Both of these approaches will become insecure when facing attackers with infinite computing power. In this paper, we develop a wireless unconditional authentication framework based

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17

Cheng, Longwang, Li Zhou, Boon-Chong Seet, Wei Li, Dongtang Ma, and Jibo Wei. "Efficient Physical-Layer Secret Key Generation and Authentication Schemes Based on Wireless Channel-Phase." Mobile Information Systems 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/7393526.

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Анотація:

Exploiting the inherent physical properties of wireless channels to complement or enhance the traditional security mechanisms has attracted prominent attention recently. However, the existing secret key generation schemes suffer from miscellaneous extracting procedure. Many PHY-layer authentication schemes assume that the knowledge of the shared key is preknown. In this paper, we propose PHY-layer secret key generation and authentication schemes for orthogonal frequency-division multiplexing (OFDM) systems. In the secret key generation scheme, to simplify the extracting procedure, only one leg

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18

Alhoraibi, Lamia, Daniyal Alghazzawi, Reemah Alhebshi, and Osama Bassam J. Rabie. "Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches." Sensors 23, no. 4 (2023): 1814. http://dx.doi.org/10.3390/s23041814.

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Анотація:

The physical layer security of wireless networks is becoming increasingly important because of the rapid development of wireless communications and the increasing security threats. In addition, because of the open nature of the wireless channel, authentication is a critical issue in wireless communications. Physical layer authentication (PLA) is based on distinctive features to provide information-theory security and low complexity. However, although many researchers are interested in the PLA and how it might be used to improve wireless security, there is surprisingly little literature on the

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19

Xie, Ning, Junjie Chen, and Lei Huang. "Physical-Layer Authentication Using Multiple Channel-Based Features." IEEE Transactions on Information Forensics and Security 16 (2021): 2356–66. http://dx.doi.org/10.1109/tifs.2021.3054534.

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20

Xie, Ning, Haijun Tan, Lei Huang, and Alex X. Liu. "Physical-Layer Authentication in Wirelessly Powered Communication Networks." IEEE/ACM Transactions on Networking 29, no. 4 (2021): 1827–40. http://dx.doi.org/10.1109/tnet.2021.3071670.

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21

Xie, Ning, and TianXing Hu. "Improving the covertness in the physical-layer authentication." China Communications 18, no. 3 (2021): 122–31. http://dx.doi.org/10.23919/jcc.2021.03.010.

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22

Wang, Ning, Ting Jiang, Shichao Lv, and Liang Xiao. "Physical-Layer Authentication Based on Extreme Learning Machine." IEEE Communications Letters 21, no. 7 (2017): 1557–60. http://dx.doi.org/10.1109/lcomm.2017.2690437.

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23

Baracca, Paolo, Nicola Laurenti, and Stefano Tomasin. "Physical Layer Authentication over MIMO Fading Wiretap Channels." IEEE Transactions on Wireless Communications 11, no. 7 (2012): 2564–73. http://dx.doi.org/10.1109/twc.2012.051512.111481.

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24

Liu, Jiazi, Ahmed Refaey, Xianbin Wang, and Helen Tang. "Reliability enhancement for CIR-based physical layer authentication." Security and Communication Networks 8, no. 4 (2014): 661–71. http://dx.doi.org/10.1002/sec.1014.

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25

Nagamani, K., and R. Monisha. "Physical Layer Security Using Cross Layer Authentication for AES-ECDSA Algorithm." Procedia Computer Science 215 (2022): 380–92. http://dx.doi.org/10.1016/j.procs.2022.12.040.

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26

Poor, H. Vincent, and Rafael F. Schaefer. "Wireless physical layer security." Proceedings of the National Academy of Sciences 114, no. 1 (2016): 19–26. http://dx.doi.org/10.1073/pnas.1618130114.

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Анотація:

Security in wireless networks has traditionally been considered to be an issue to be addressed separately from the physical radio transmission aspects of wireless systems. However, with the emergence of new networking architectures that are not amenable to traditional methods of secure communication such as data encryption, there has been an increase in interest in the potential of the physical properties of the radio channel itself to provide communications security. Information theory provides a natural framework for the study of this issue, and there has been considerable recent research de

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27

Wang, Ge, Shouqian Shi, Minmei Wang, et al. "RF-Chain." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, no. 4 (2022): 1–28. http://dx.doi.org/10.1145/3569493.

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Blockchain-based supply chains provide a new solution to decentralized multi-party product management. However, existing methods, including ID-based and cryptographic-based solutions, cannot achieve both counterfeit resistance and decentralization in supply chain management. We argue that this dilemma comes from the disconnection and inconsistency of the data records and physical product entities. This paper proposes RF-Chain, a novel decentralized supply chain management solution that uniquely combines data record authentication and physical-layer RFID tag authentication to effectively achiev

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28

Bai, Lin, Lina Zhu, Jianwei Liu, Jinho Choi, and Wei Zhang. "Physical layer authentication in wireless communication networks: A survey." Journal of Communications and Information Networks 5, no. 3 (2020): 237–64. http://dx.doi.org/10.23919/jcin.2020.9200889.

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29

Xie, Ning, Shengli Zhang, and Alex X. Liu. "Physical-Layer Authentication in Non-Orthogonal Multiple Access Systems." IEEE/ACM Transactions on Networking 28, no. 3 (2020): 1144–57. http://dx.doi.org/10.1109/tnet.2020.2979058.

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30

Fang, He, Xianbin Wang, and Lajos Hanzo. "Learning-Aided Physical Layer Authentication as an Intelligent Process." IEEE Transactions on Communications 67, no. 3 (2019): 2260–73. http://dx.doi.org/10.1109/tcomm.2018.2881117.

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31

Yu, Paul L., and Brian M. Sadler. "MIMO Authentication via Deliberate Fingerprinting at the Physical Layer." IEEE Transactions on Information Forensics and Security 6, no. 3 (2011): 606–15. http://dx.doi.org/10.1109/tifs.2011.2134850.

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32

Liu, Jiazi, and Xianbin Wang. "Physical Layer Authentication Enhancement Using Two-Dimensional Channel Quantization." IEEE Transactions on Wireless Communications 15, no. 6 (2016): 4171–82. http://dx.doi.org/10.1109/twc.2016.2535442.

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33

Wen, Hong, Yifan Wang, Liang Zhou, Xiping Zhu, and Jianqiang Li. "Physical layer assist authentication technique for smart meter system." IET Communications 7, no. 3 (2013): 189–97. http://dx.doi.org/10.1049/iet-com.2012.0300.

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34

Althunibat, Saud, Victor Sucasas, Georgios Mantas, and Jonathan Rodriguez. "Physical-layer entity authentication scheme for mobile MIMO systems." IET Communications 12, no. 6 (2018): 712–18. http://dx.doi.org/10.1049/iet-com.2017.0518.

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35

Qiu, Xiaoying, Ting Jiang, Sheng Wu, and Monson Hayes. "Physical Layer Authentication Enhancement Using a Gaussian Mixture Model." IEEE Access 6 (2018): 53583–92. http://dx.doi.org/10.1109/access.2018.2871514.

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36

Chen, Dajiang, Ning Zhang, Nan Cheng, Kuan Zhang, Zhiguang Qin, and Xuemin Shen. "Physical Layer based Message Authentication with Secure Channel Codes." IEEE Transactions on Dependable and Secure Computing 17, no. 5 (2020): 1079–93. http://dx.doi.org/10.1109/tdsc.2018.2846258.

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37

Tomasin, Stefano, Hongliang Zhang, Arsenia Chorti, and H. Vincent Poor. "Challenge-Response Physical Layer Authentication over Partially Controllable Channels." IEEE Communications Magazine 60, no. 12 (2022): 138–44. http://dx.doi.org/10.1109/mcom.001.2200339.

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38

Liu, Jiazi, Xianbin Wang, and Helen Tang. "Physical Layer Authentication Enhancement Using Maximum SNR Ratio Based Cooperative AF Relaying." Wireless Communications and Mobile Computing 2017 (2017): 1–16. http://dx.doi.org/10.1155/2017/7206187.

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Анотація:

Physical layer authentication techniques developed in conventional macrocell wireless networks face challenges when applied in the future fifth-generation (5G) wireless communications, due to the deployment of dense small cells in a hierarchical network architecture. In this paper, we propose a novel physical layer authentication scheme by exploiting the advantages of amplify-and-forward (AF) cooperative relaying, which can increase the coverage and convergence of the heterogeneous networks. The essence of the proposed scheme is to select the best relay among multiple AF relays for cooperation

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39

Hazratifard, Mehdi, Fayez Gebali, and Mohammad Mamun. "Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial." Sensors 22, no. 19 (2022): 7655. http://dx.doi.org/10.3390/s22197655.

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Анотація:

Telehealth systems have evolved into more prevalent services that can serve people in remote locations and at their homes via smart devices and 5G systems. Protecting the privacy and security of users is crucial in such online systems. Although there are many protocols to provide security through strong authentication systems, sophisticated IoT attacks are becoming more prevalent. Using machine learning to handle biometric information or physical layer features is key to addressing authentication problems for human and IoT devices, respectively. This tutorial discusses machine learning applica

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40

Xia, Shida, Xiaofeng Tao, Na Li, et al. "Multiple Correlated Attributes Based Physical Layer Authentication in Wireless Networks." IEEE Transactions on Vehicular Technology 70, no. 2 (2021): 1673–87. http://dx.doi.org/10.1109/tvt.2021.3055563.

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41

Perazzone, Jake Bailey, Paul L. Yu, Brian M. Sadler, and Rick S. Blum. "Artificial Noise-Aided MIMO Physical Layer Authentication With Imperfect CSI." IEEE Transactions on Information Forensics and Security 16 (2021): 2173–85. http://dx.doi.org/10.1109/tifs.2021.3050599.

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42

Xiao, Liang, Xiaozhen Lu, Tangwei Xu, Weihua Zhuang, and Huaiyu Dai. "Reinforcement Learning-Based Physical-Layer Authentication for Controller Area Networks." IEEE Transactions on Information Forensics and Security 16 (2021): 2535–47. http://dx.doi.org/10.1109/tifs.2021.3056206.

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43

Refaey, Ahmed, Weikun Hou, and Khaled Loukhaoukha. "Multilayer Authentication for Communication Systems Based on Physical-Layer Attributes." Journal of Computer and Communications 02, no. 08 (2014): 64–75. http://dx.doi.org/10.4236/jcc.2014.28007.

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44

Lu, Xinjin, Jing Lei, Yuxin Shi, and Wei Li. "Improved Physical Layer Authentication Scheme Based on Wireless Channel Phase." IEEE Wireless Communications Letters 11, no. 1 (2022): 198–202. http://dx.doi.org/10.1109/lwc.2021.3123820.

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45

Zhang, Pinchang, Tarik Taleb, Xiaohong Jiang, and Bin Wu. "Physical Layer Authentication for Massive MIMO Systems With Hardware Impairments." IEEE Transactions on Wireless Communications 19, no. 3 (2020): 1563–76. http://dx.doi.org/10.1109/twc.2019.2955128.

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46

Wang, Ning, Weiwei Li, Pu Wang, Amir Alipour-Fanid, Long Jiao, and Kai Zeng. "Physical Layer Authentication for 5G Communications: Opportunities and Road Ahead." IEEE Network 34, no. 6 (2020): 198–204. http://dx.doi.org/10.1109/mnet.011.2000122.

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47

Verma, Gunjan, Paul Yu, and Brian M. Sadler. "Physical Layer Authentication via Fingerprint Embedding Using Software-Defined Radios." IEEE Access 3 (2015): 81–88. http://dx.doi.org/10.1109/access.2015.2398734.

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48

Ran, Yachao, Harith Al-Shwaily, Chaoqing Tang, Gui Yun Tian, and Martin Johnston. "Physical layer authentication scheme with channel based tag padding sequence." IET Communications 13, no. 12 (2019): 1776–80. http://dx.doi.org/10.1049/iet-com.2018.5749.

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49

Gao, Ning, Qiang Ni, Daquan Feng, Xiaojun Jing, and Yue Cao. "Physical layer authentication under intelligent spoofing in wireless sensor networks." Signal Processing 166 (January 2020): 107272. http://dx.doi.org/10.1016/j.sigpro.2019.107272.

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50

Wang, Yupeng, Jianfeng Jin, Yufeng Li, and Chang Choi. "A Reliable Physical Layer Authentication Algorithm for Massive IoT Systems." IEEE Access 8 (2020): 80684–90. http://dx.doi.org/10.1109/access.2020.2989395.

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