Machine Learning for Intelligent Authentication in 5G and Beyond Wireless Networks
The 5G and beyond wireless networks are critical to support diverse vertical applications by connecting heterogeneous devices and machines, which directly increase vulnerability for various spoofing attacks. Conventional cryptographic and physical layer authentication techniques are facing some c...
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| Ngôn ngữ: | en_US |
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IEEE Xplore
2020
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| Truy cập trực tuyến: | http://tailieuso.tlu.edu.vn/handle/DHTL/9873 |
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oai:localhost:DHTL-98732020-12-09T03:53:12Z Machine Learning for Intelligent Authentication in 5G and Beyond Wireless Networks Fang, H. Wang, X. Tomasin,S. Machine learning wireless networks Conventional cryptographic intelligent authentication The 5G and beyond wireless networks are critical to support diverse vertical applications by connecting heterogeneous devices and machines, which directly increase vulnerability for various spoofing attacks. Conventional cryptographic and physical layer authentication techniques are facing some challenges in complex dynamic wireless environments, including significant security overhead, low reliability, as well as difficulties in pre-designing a precise authentication model, providing continuous protection, and learning time-varying attributes. In this article, we envision new authentication approaches based on machine learning techniques by opportunistically leveraging physical layer attributes, and introduce intelligence to authentication for more efficient security provisioning. Machine learning paradigms for intelligent authentication design are presented, namely for parametric/non-parametric and supervised/unsupervised/reinforcement learning algorithms. In a nutshell, the machine-learning-based intelligent authentication approaches utilize specific features in the multi-dimensional domain for achieving cost-effective, more reliable, model-free, continuous, and situation-aware device validation under unknown network conditions and unpredictable dynamics. 2020-12-09T03:52:12Z 2020-12-09T03:52:12Z 2019 BB http://tailieuso.tlu.edu.vn/handle/DHTL/9873 en_US IEEE Wireless Communications, (2019), Vol 26, Issue 5, pp 55-61 application/pdf IEEE Xplore |
| institution |
Trường Đại học Thủy Lợi |
| collection |
DSpace |
| language |
en_US |
| topic |
Machine learning wireless networks Conventional cryptographic intelligent authentication |
| spellingShingle |
Machine learning wireless networks Conventional cryptographic intelligent authentication Fang, H. Machine Learning for Intelligent Authentication in 5G and Beyond Wireless Networks |
| description |
The 5G and beyond wireless networks are critical to support diverse vertical applications by
connecting heterogeneous devices and machines, which directly increase vulnerability for various
spoofing attacks. Conventional cryptographic and physical layer authentication techniques are facing
some challenges in complex dynamic wireless environments, including significant security
overhead, low reliability, as well as difficulties in pre-designing a precise authentication model, providing
continuous protection, and learning time-varying attributes. In this article, we envision new authentication
approaches based on machine learning techniques by opportunistically leveraging physical layer attributes,
and introduce intelligence to authentication for more efficient security provisioning. Machine learning paradigms
for intelligent authentication design are presented, namely for parametric/non-parametric and supervised/unsupervised/reinforcement learning algorithms. In a nutshell, the machine-learning-based
intelligent authentication approaches utilize specific features in the multi-dimensional domain for
achieving cost-effective, more reliable, model-free, continuous, and situation-aware device validation
under unknown network conditions and unpredictable dynamics. |
| author2 |
Wang, X. |
| author_facet |
Wang, X. Fang, H. |
| format |
BB |
| author |
Fang, H. |
| author_sort |
Fang, H. |
| title |
Machine Learning for Intelligent Authentication in 5G and Beyond Wireless Networks |
| title_short |
Machine Learning for Intelligent Authentication in 5G and Beyond Wireless Networks |
| title_full |
Machine Learning for Intelligent Authentication in 5G and Beyond Wireless Networks |
| title_fullStr |
Machine Learning for Intelligent Authentication in 5G and Beyond Wireless Networks |
| title_full_unstemmed |
Machine Learning for Intelligent Authentication in 5G and Beyond Wireless Networks |
| title_sort |
machine learning for intelligent authentication in 5g and beyond wireless networks |
| publisher |
IEEE Xplore |
| publishDate |
2020 |
| url |
http://tailieuso.tlu.edu.vn/handle/DHTL/9873 |
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AT fangh machinelearningforintelligentauthenticationin5gandbeyondwirelessnetworks |
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