Attack-Statistic Adaptability Evaluation of Wavelet Denoising Methods in Side-Channel Power Analysis

Wavelet denoising is used before side-channel power analysis, but SNR/MSE may not reflect key recovery. This paper evaluates five wavelet methods for measurement-driven DPA/CPA evaluation. On ATMEGA16A AES-128 traces, TH, MOD, TI, WP and GHM are compared under the same dataset and attack flow, using peak separability, correlation stability, trace demand and cost. Denoising and attack rankings differ. In first-byte DPA, GHM achieves 100% success with 387 traces and DPA-SNR about 21.7; TI needs 502 traces and reaches 17.8. In 16-byte CPA, TI performs best overall, with a mean correlation 0.2719 and cumulative trace demand of 2921; MOD has the lowest maximum byte threshold, 647. No method is optimal for all attack statistics. DPA favors peak fidelity and false-peak suppression, CPA favors covariance stability and shift-invariant smoothing, and TI is a practical compromise under attack-path or cost constraints. These conclusions are framed as dataset- and attack-statistic-specific evidence for the measured unprotected ATMEGA16A AES-128 implementation, rather than as a universal ranking of wavelet denoising methods.