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This paper investigates the capacity of physical-layer fingerprints for 100BASE-TX devices in Industrial Internet of Things (IIoT) networks, addressing the critical need for reliable device authentication to prevent impersonation. Utilizing a nonlinear and impulse-response model (NAIM), the authors derive a fingerprint-space capacity of approximately $2.96\times10^{10}$ distinguishable states, revealing the constraints imposed by transmitter waveform requirements and noise. Experimental validation with 48 network interface cards (NICs) shows that higher empirical capacity correlates with improved identification accuracy, underscoring the model's practical utility for pre-deployment assessments in IIoT environments.
The fingerprint-space capacity for 100BASE-TX devices is a staggering $2.96\times10^{10}$ distinguishable states, crucial for enhancing device authentication in IIoT.
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.
