To ensure secure and reliable communication in wireless systems, authenticating the identities of numerous nodes is imperative. Traditional cryptography-based authentication methods suffer from issues such as low compatibility, reliability, and high complexity. Physical-Layer Authentication (PLA) is emerging as a promising complement due to its exploitation of unique properties in wireless environments. Recently, Machine Learning (ML)-based PLA has gained attention for its intelligence, adaptability, universality, and scalability compared to non-ML approaches. However, a comprehensive overview of state-of-the-art ML-based PLA and its foundational aspects is lacking. This paper presents a comprehensive survey of characteristics and technologies that can be used in the ML-based PLA. We categorize existing ML-based PLA schemes into two main types: multi-device identification and attack detection schemes. In deep learning-based multi-device identification schemes, Deep Neural Networks are employed to train models, avoiding complex processing and expert feature transformation. Deep learning-based multi-device identification schemes are further subdivided, with schemes based on Convolutional Neural Networks being extensively researched. In ML-based attack detection schemes, receivers utilize intelligent ML techniques to set detection thresholds automatically, eliminating the need for manual calculation or knowledge of channel models. ML-based attack detection schemes are categorized into three sub-types: Supervised Learning, Unsupervised Learning, and Reinforcement Learning. Additionally, we summarize open-source datasets used for PLA, encompassing Radio Frequency fingerprints and channel fingerprints. Finally, this paper outlines future research directions to guide researchers in related fields.

中文翻译：

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无线通信中基于机器学习的物理层身份验证调查


为了确保无线系统中安全可靠的通信，必须验证众多节点的身份。传统的密码认证方法存在兼容性低、可靠性低、复杂度高等问题。物理层身份验证 （PLA） 由于在无线环境中利用了独特的特性，因此正在成为一种很有前途的补充。最近，与非 ML 方法相比，基于机器学习 （ML） 的 PLA 因其智能性、适应性、通用性和可扩展性而受到关注。然而，缺乏对最先进的基于 ML 的 PLA 及其基础方面的全面概述。本文对可用于基于 ML 的 PLA 的特性和技术进行了全面调查。我们将现有的基于 ML 的 PLA 方案分为两种主要类型：多设备识别和攻击检测方案。在基于深度学习的多设备识别方案中，采用深度神经网络来训练模型，避免了复杂的处理和专家特征转换。基于深度学习的多设备识别方案进一步细分，基于卷积神经网络的方案得到了广泛的研究。在基于 ML 的攻击检测方案中，接收方利用智能 ML 技术自动设置检测阈值，无需手动计算或了解信道模型。基于 ML 的攻击检测方案分为三个子类型：监督学习、无监督学习和强化学习。此外，我们还总结了用于 PLA 的开源数据集，包括射频指纹和信道指纹。 最后，本文概述了未来的研究方向，以指导相关领域的研究人员。