arXiv 2004.111262020-04-23eess.SP

通过深度神经网络利用硬件受损的带外信息进行稳健的无线设备分类

Leveraging Hardware-Impaired Out-of-Band Information Through Deep Neural Networks for Robust Wireless Device Classification

Abdurrahman Elmaghbub, Bechir Hamdaoui

备注：: 9 pages

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摘要：

无线设备分类技术在促进新兴的无线应用中起着关键作用，例如允许频谱管理机构执行其访问策略，并使网络管理员能够控制对其无线网络的访问并防止对其无线网络的冒充攻击。利用由制造和组装阶段期间产生的收发器硬件损伤引起的发射RF信号的频谱失真来提供设备分类已经成为许多最近工作的焦点。这些先前的工作本质上是应用深度学习从硬件受损的信号中提取设备的特征，并依赖于设备之间的特征变化来区分设备。随着技术的进步，设备之间的制造减值差异变得非常微不足道，使得这些先前的分类方法不准确。本文提出了一种基于深度学习的新型技术，该技术可提供可扩展且高度准确的无线设备分类，即使这些设备在其硬件损伤方面表现出微不足道的变化，并且具有相同的硬件，协议和软件配置。所提出的技术的新颖性在于通过在接收器处对捕获的信号进行过采样并将从RF信号收集的IQ样本馈送到深度神经网络以进行分类来利用{ \em带内}和{ \em带外}信号失真信息。使用卷积神经网络（CNN）模型，我们表明，我们提出的技术，当应用于高端，高性能的设备与最小失真的硬件，设备分类准确性相比，现有的方法翻了一番。

英文摘要：

Wireless device classification techniques play a key role in promoting emerging wireless applications such as allowing spectrum regulatory agencies to enforce their access policies and enabling network administrators to control access and prevent impersonation attacks to their wireless networks. Leveraging spectrum distortions of transmitted RF signals, caused by transceiver hardware impairments created during manufacture and assembly stages, to provide device classification has been the focus of many recent works. These prior works essentially apply deep learning to extract features of the devices from their hardware-impaired signals and rely on feature variations across the devices to distinguish devices from one another. As technology advances, the manufacturing impairment variations across devices are becoming extremely insignificant, making these prior classification approaches inaccurate. This paper proposes a novel, deep learning-based technique that provides scalable and highly accurate classification of wireless devices, even when the devices exhibit insignificant variation across their hardware impairments and have the same hardware, protocol, and software configurations. The novelty of the proposed technique lies in leveraging both the {\em in-band} and {\em out-of-band} signal distortion information by oversampling the captured signals at the receiver and feeding IQ samples collected from the RF signals to a deep neural network for classification. Using a convolutional neural network (CNN) model, we show that our proposed technique, when applied to high-end, high-performance devices with minimally distorted hardware, doubles the device classification accuracy when compared to existing approaches.

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