# Physical Layer Authentication and Security Design in the Machine Learning Era

arXiv: https://arxiv.org/abs/2305.09748 · HTML: https://mlsanity.com/paper/2305.09748/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2023.

## Built on by (most cited first, 6 of 6)

- [One-Class Classification as GLRT for Jamming Detection in Private 5G Networks](https://mlsanity.com/paper/2405.09565/) (2024)
- [Practical Physical Layer Authentication for Mobile Scenarios Using a Synthetic Dataset Enhanced Deep Learning Approach](https://mlsanity.com/paper/2508.20861/) (2025)
- [Model-Driven Learning-Based Physical Layer Authentication for Mobile Wi-Fi Devices](https://mlsanity.com/paper/2603.19972/) (2026)
- [ML-Enabled Eavesdropper Detection in Beyond 5G IIoT Networks](https://mlsanity.com/paper/2505.07837/) (2025)
- [Distributed Physical Layer Authentication and Collaborative RSMA in Non-Terrestrial Networks via Graph Reinforcement Learning](https://mlsanity.com/paper/2609.09475/) (2026)
- [Adaptive Distributed Physical-Layer Authentication and Attack Detection in 6G Non-Terrestrial Networks via Causal Meta-Learning](https://mlsanity.com/paper/2609.09511/) (2026)

## Abstract (arXiv)

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.