AI 中文总结
本文针对IIoT中100BASE-TX设备，提出NAIM模型分析其物理层指纹空间容量，推导得约2.96×10^10个可区分状态，实验验证其可用于部署前评估。
AI 中文摘要
工业互联网（IIoT）网络广泛采用100BASE-TX等以太网技术进行工业通信。随着工业网络规模不断扩大，可靠的设备认证对于防止设备假冒和未授权访问愈发重要。物理层指纹识别（PLF）利用传输信号中依赖设备的指纹特征，提供基于硬件的终端认证方法。然而，100BASE-TX物理层指纹所支持的可区分空间及其容量边界在很大程度上尚未被探索。为分析物理层指纹的容量，本文提出非线性脉冲响应模型（NAIM），该模型可表征100BASE-TX传输波形中依赖设备的波形差异：非线性分量捕获稳态电平偏差，脉冲响应分量描述电平转换期间的过渡响应。100BASE-TX发射机波形要求、由噪声和模数转换（ADC）量化决定的观测分辨率，以及目标误码率（BER）共同约束了可允许的指纹空间。在NAIM模型下，100BASE-TX终端的指纹空间容量被推导为约2.96×10^10个可区分状态。对来自48个网络接口卡（NIC）在两种线缆条件下采集的信号开展实验，基于测得的设备间和设备内变化估计出高斯等效经验容量。在5米线缆条件下，经验容量和闭集识别对三种NIC模型的排序一致，且更大的经验容量对应更高的识别准确率。这些结果表明，所提出的容量分析可为IIoT中的物理层指纹识别提供部署前评估。
英文摘要
Industrial Internet of Things (IIoT) networks widely adopt Ethernet technologies, such as 100BASE-TX, for industrial communications. As industrial networks continue to scale, reliable device authentication becomes increasingly important for preventing device impersonation and unauthorized access. Physical-layer fingerprinting (PLF) exploits device-dependent fingerprint features in transmitted signals and provides a hardware-based approach for terminal authentication. However, the distinguishable space supported by 100BASE-TX physical-layer fingerprints and its capacity boundary remain largely unexplored. To analyze the capacity of physical-layer fingerprints, this paper proposes a nonlinear and impulse-response model (NAIM) that characterizes device-dependent waveform differences in 100BASE-TX transmitted waveforms. The nonlinear component captures steady-state level deviations, while the impulse-response component describes the transition response during level transitions. The 100BASE-TX transmitter waveform requirements, the observation resolution determined by noise and analog-to-digital conversion (ADC) quantization, and the target bit-error ratio (BER) constrain the admissible fingerprint space. Under the NAIM model, the fingerprint-space capacity of 100BASE-TX terminals is derived as approximately $2.96\times10^{10}$ distinguishable states. Experiments on signals collected from 48 NICs under two cable conditions estimate a Gaussian-equivalent empirical capacity from the measured inter-device and within-device variations. Under the 5-m cable condition, empirical capacity and closed-set identification consistently rank the three NIC models, and a larger empirical capacity yields higher identification accuracy. These results demonstrate that the proposed capacity analysis provides a pre-deployment assessment for physical-layer fingerprinting in IIoT.
Comments12 pages, 7 figures, 5 tables. Submitted to IEEE Internet of Things Journal
