Machine Learning for Intelligent Authentication in 5G-and-Beyond Wireless Networks
This addresses security challenges for wireless network operators and users, but it appears incremental as it applies existing ML paradigms to a specific domain.
The paper tackles the problem of authentication vulnerabilities in 5G-and-beyond wireless networks by proposing machine learning-based approaches that leverage physical layer attributes, aiming to achieve cost-effective, reliable, model-free, continuous, and situation-aware device validation.
The fifth generation (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 difficulty in pre-designing authentication model, providing continuous protections, 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.