Machine Learning for Intelligent Authentication in 5G and Beyond Wireless Networks

Abstract

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.