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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| Định dạng: | BB |
| Ngôn ngữ: | en_US |
| Thông tin xuất bản: |
IEEE Xplore
2020
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| Chủ đề: | |
| Truy cập trực tuyến: | http://tailieuso.tlu.edu.vn/handle/DHTL/9873 |
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| Tóm tắt: | 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. |
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