Beyond TVLA: Anderson-Darling Leakage Assessment for Neural Network Side-Channel Leakage Detection

Abstract

Test Vector Leakage Assessment (TVLA) is widely used for side-channel leakage detection, but its reliance on Welch's t-test makes it primarily sensitive to differences in the means of two leakage populations. Consequently, TVLA may fail to detect leakage that manifests through changes in other characteristics of the underlying distributions. We introduce Anderson-Darling Leakage Assessment (ADLA), a distribution-sensitive leakage assessment methodology based on the two-sample Anderson-Darling test. To facilitate direct comparison with conventional TVLA, we derive an ADLA decision threshold corresponding to the nominal significance level associated with the standard TVLA threshold of 4.5. We evaluate ADLA on a shuffling-protected embedded multilayer perceptron implementation under both fixed-versus-fixed and fixed-versus-random input configurations. Across the evaluated settings, ADLA produces clearer threshold exceedances than TVLA and reveals leakage locations that are not detected by the mean-based test. To assess the practical relevance of these additional leakage locations, we perform correlation power analysis using points of interest selected from the ADLA and TVLA statistics. The points identified by ADLA enable recovery of the exponent byte of the targeted model weight despite the presence of shuffling. These results demonstrate that distribution-sensitive testing can complement conventional TVLA by revealing exploitable side-channel leakage that may remain hidden from mean-based analysis.