Visibility graph entropy based radiometric feature for physical layer identification
Visibility graph entropy based radiometric feature for physical layer identification
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DOI：
10.1016/j.adhoc.2022.102780
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发表时间：
2022-01-18
期刊：
影响因子：
4.8
通讯作者：
Shiratori, Norio
中科院分区：
文献类型：
--
作者：
Zeng, Shuiguang;Chen, Yin;Shiratori, Norio
新兴的物理层识别方法通过利用无线设备中随机生成的硬件缺陷导致的不可控、不可克隆和不可伪造的辐射特征，证明了补充和增强物联网网络设备认证的能力。多特征识别已被证明是提高识别性能的一种有效可行的方法。与此同时，缺乏有效的设备识别辐射特征是一个主要问题。现有的特征大多是从时间域、频率域或相位域的角度推导出来的。在本研究中，我们探讨了无线帧序文的图域，并提出了一种新的辐射测量特征，称为归一化水平可见图香农熵（HVGE）。首先，我们引入了一个由样本截断和下采样组成的预处理，以便在可见性图（VG）转换的计算时间和识别性能之间进行调整。其次，从VG表示出发，提出了新的HVGE特征的计算方法。最后，利用50个现成的无线设备进行了实验研究，探讨了预处理参数以及噪声和特征组合对识别性能增益的影响。
Emerging physical layer identification methods have demonstrated the capability to complement and enhance the device authentication of Internet of Things networks by exploiting the uncontrollable, unclonable, and unforgeable radiometric features resulted from randomly generated hardware imperfection in wireless devices. Multiple feature-based identification has proven an efficient and feasible approach to improving identification performance. At the same time, the lack of radiometric features effective for device identification is a major problem. Most of the existing features are derived from the view of the time, frequency, or phase domain. In this study, we explore the graph domain of wireless frame's preambles and propose a new radiometric feature called normalized horizontal visibility graph Shannon entropy (HVGE). At first, we introduce a preprocessing consisting of sample truncation and downsampling to enable the adjustment between the computational time of visibility graph (VG) conversion and the identification performance. Secondly, we propose the calculation method of the new HVGE feature from the VG representation. Finally, an experimental study using 50 off-the-shelf wireless devices was conducted to investigate the impact of the preprocessing parameters and the effect of noise and feature combinations on the identification performance gain.