Unlike most of the upper layer authentication mechanisms, the physical (PHY) layer authentication takes advantages of channel impulse response from wireless propagation to identify transmitted packages with low-resource consumption, and machine learning methods are effective ways to improve its implementation. However, the training of the machine-learning-based PHY-layer authentication requires a large number of training samples, which makes the training process time consuming and computationally resource intensive. In this article, we propose a data augmented multiuser PHY-layer authentication scheme to enhance the security of mobile-edge computing system, an emergent architecture in the Internet of Things (IoT). Three data augmentation algorithms are proposed to speed up the establishment of the authentication model and improve the authentication success rate. By combining the deep neural network with data augmentation methods, the performance of the proposed multiuser PHY-layer authentication scheme is improved and the training speed is accelerated, even with fewer training samples. Extensive simulations are conducted under the real industry IoT environment and the figures illustrate the effectiveness of our approach.

中文翻译：

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具有数据增强的物联网中的多用户物理层身份验证


与大多数上层认证机制不同，物理（PHY）层认证利用无线传播的信道脉冲响应来识别低资源消耗的传输包，而机器学习方法是改进其实现的有效方法。然而，基于机器学习的PHY层认证的训练需要大量的训练样本，这使得训练过程耗时且计算资源密集。在本文中，我们提出了一种数据增强的多用户 PHY 层身份验证方案，以增强移动边缘计算系统（物联网（IoT）中的一种新兴架构）的安全性。提出了三种数据增强算法来加速认证模型的建立并提高认证成功率。通过将深度神经网络与数据增强方法相结合，即使训练样本较少，所提出的多用户 PHY 层认证方案的性能也得到了提高，训练速度也加快了。在真实的工业物联网环境下进行了广泛的模拟，数据说明了我们方法的有效性。