Physical Layer Authentication and Security Design in the Machine Learning Era

TL;DR

This paper reviews how machine learning techniques are transforming physical layer security in wireless systems, focusing on authentication and secure transmission, and discusses challenges and future opportunities in this emerging field.

Contribution

It provides a comprehensive overview of ML algorithms suitable for PHY security and analyzes recent advances, including neural network applications, highlighting new research directions.

Findings

ML algorithms effectively enhance PHY security

Neural networks can optimize PHY security problems

Identification of key challenges and future opportunities

Abstract

Security at the physical layer (PHY) is a salient research topic in wireless systems, and machine learning (ML) is emerging as a powerful tool for providing new data-driven security solutions. Therefore, the application of ML techniques to the PHY security is of crucial importance in the landscape of more and more data-driven wireless services. In this context, we first summarize the family of bespoke ML algorithms that are eminently suitable for wireless security. Then, we review the recent progress in ML-aided PHY security, where the term "PHY security" is classified into two different types: i) PHY authentication and ii) secure PHY transmission. Moreover, we treat neural networks as special types of ML and present how to deal with PHY security optimization problems using neural networks. Finally, we identify some major challenges and opportunities in tackling PHY security challenges by applying carefully tailored ML tools.