Radio Frequency Fingerprinting for WiFi Authentication Based on Detrended Fluctuation Analysis

Traditional physical-layer authentication (PLA) approaches primarily rely on a limited set of hardware features, which restricts their robustness and accuracy in dynamic wireless environments. This paper proposes a novel PLA enhancement framework based on nonlinear signal analysis, introducing detrended fluctuation analysis (DFA) as a distinctive hardware fingerprinting feature. We first model the statistical properties of DFA and analytically derive its dependence on intrinsic hardware imperfections, thereby establishing its feasibility for device authentication. To improve overall system performance, the DFA feature is further fused with conventional features such as fractal dimension and carrier frequency offset (CFO) within a shallow classification framework. Experimental evaluation is conducted on real signal data collected from 28 commercial devices, demonstrating that the proposed multifeature PLA scheme can significantly improve authentication accuracy, confirming the effectiveness of DFA in enhancing physical-layer security without additional cryptographic overhead.