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 NNs as special types of ML and present how to deal with PHY security optimization problems using NNs. Finally, we identify some major challenges and opportunities in tackling PHY security challenges by applying carefully tailored ML tools.

中文翻译：

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机器学习时代的物理层认证与安全设计


物理层 (PHY) 安全是无线系统中的一个突出研究主题，而机器学习 (ML) 正在成为提供新的数据驱动安全解决方案的强大工具。因此，在越来越多的数据驱动的无线服务领域，将机器学习技术应用于 PHY 安全至关重要。在这种情况下，我们首先总结了非常适合无线安全的定制机器学习算法系列。然后，我们回顾了 ML 辅助 PHY 安全性的最新进展，其中术语“PHY 安全性”分为两种不同类型：i) PHY 身份验证和 ii) 安全 PHY 传输。此外，我们将神经网络视为特殊类型的机器学习，并介绍如何使用神经网络处理 PHY 安全优化问题。最后，我们确定了通过应用精心定制的 ML 工具来应对 PHY 安全挑战的一些主要挑战和机遇。