论文标题
使用频道状态信息和机器学习的物理层认证
Physical-Layer Authentication Using Channel State Information and Machine Learning
论文作者
论文摘要
在互连的无线环境中，强有力的身份验证仍然是一个重要但有时难以捉摸的目标。使用频道功能的物理层认证研究将有望作为提高各种设备网络安全的一种技术。我们建议使用机器学习并测量多输入多输出通信渠道信息，以决定是否对特定设备进行身份验证。这项工作分析了从无线环境中接收到的频道状态信息的使用，并证明了经过接收的频道数据训练的生成对抗神经网络（GAN）的使用以身份验证传输设备。我们比较了各种机器学习技术，发现局部离群因子（LOF）算法在较低的信号与噪声比（SNR）时达到100％精度，而不是其他算法。但是，在LOF达到100％之前，我们还表明GAN在较低的SNR水平下更准确。
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. This work analyzes the use of received channel state information from the wireless environment and demonstrates the employment of a generative adversarial neural network (GAN) trained with received channel data to authenticate a transmitting device. We compared a variety of machine learning techniques and found that the local outlier factor (LOF) algorithm reached 100% accuracy at lower signal to noise ratios (SNR) than other algorithms. However, before LOF reached 100%, we also show that the GAN was more accurate at lower SNR levels.
