Strong authentication in an interconnected wireless environment continues to be an important, but sometimes elusive goal. Research in physical-layer authentication using channel features holds promise as a technique to improve network security for a variety of devices. We propose the use of machine learning and measured multiple-input multiple-output communications channel information to make a decision on whether or not to authenticate a particular device. Our approach uses received channel state information to train a neural network in an adversarial setting. These characteristics are then used to maintain authentication in subsequent communication sessions. This work analyzes the use of information from the wireless environment for the purpose of authentication and demonstrates the employment of a generative adversarial neural network trained with received channel data to authenticate a transmitting device without prior knowledge of receiver noise.

本文提出了利用物理层认证技术和机器学习对无线网络环境实现强认证的方法，分析了接收到的无线信道状态信息，通过生成对抗性神经网络 (GAN) 来验证传输设备，其中使用局部离群点因子 (LOF) 算法在低信噪比下达到了 100% 的准确率。

利用信道状态信息和机器学习进行物理层认证