  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.
