论文标题
发现基于深度学习的无线设备指纹的可移植性限制
Uncovering the Portability Limitation of Deep Learning-Based Wireless Device Fingerprints
论文作者
论文摘要
最近的设备指纹方法依赖于深度学习来仅从原始RF信号中提取特定于设备的特征，以识别，分类和身份验证无线设备。一个众所周知的问题在于，当在不同的部署域中收集培训数据和测试数据时，这些方法无法保持良好的性能。例如，当学习模型对从一个接收器收集但对从其他接收器收集的数据进行测试的数据进行培训时，与使用相同接收器收集训练和测试数据的数据相比，性能大大降低了。当考虑其他变化域（例如通道条件和协议配置）时，也会发生同样的情况。在本文中，我们首先通过测试床实验来解释这些指纹技术在域可移植性方面所面临的挑战。然后，我们将提出一些有关如何解决这些挑战的想法，以使基于学习的设备指纹对域变异性更具弹性。
Recent device fingerprinting approaches rely on deep learning to extract device-specific features solely from raw RF signals to identify, classify and authenticate wireless devices. One widely known issue lies in the inability of these approaches to maintain good performances when the training data and testing data are collected under varying deployment domains. For example, when the learning model is trained on data collected from one receiver but tested on data collected from a different receiver, the performance degrades substantially compared to when both training and testing data are collected using the same receiver. The same also happens when considering other varying domains, like channel condition and protocol configuration. In this paper, we begin by explaining, through testbed experiments, the challenges these fingerprinting techniques face when it comes to domain portability. We will then present some ideas on how to go about addressing these challenges so as to make deep learning-based device fingerprinting more resilient to domain variability.
