Test Vector Leakage Assessment (TVLA) based on Welch's $t$-test has become a standard tool for detecting side-channel leakage. However, its mean-based nature can limit sensitivity when leakage manifests primarily through higher-order distributional differences. As our experiments show, this property becomes especially crucial when it comes to evaluating neural network implementations. In this work, we propose Anderson--Darling Leakage Assessment (ADLA), a leakage detection framework that applies the two-sample Anderson--Darling test for leakage detection. Unlike TVLA, ADLA tests equality of the full cumulative distribution functions and does not rely on a purely mean-shift model. We evaluate ADLA on a multilayer perceptron (MLP) trained on MNIST and implemented on a ChipWhisperer-Husky evaluation platform. We consider protected implementations employing shuffling and random jitter countermeasures. Our results show that ADLA can provide improved leakage-detection sensitivity in protected implementations for a low number of traces compared to TVLA.

翻译：基于韦尔奇$t$检验的测试向量泄漏评估（TVLA）已成为检测侧信道泄漏的标准工具。然而，其基于均值的本质在泄漏主要通过高阶分布差异显现时会限制灵敏度。正如我们的实验所示，这一特性在评估神经网络实现时尤为关键。本文提出安德森-达林泄漏评估（ADLA），一种应用双样本安德森-达林检验进行泄漏检测的框架。与TVLA不同，ADLA检验完整累积分布函数的等同性，不依赖纯粹的均值偏移模型。我们在基于MNIST训练的多层感知器（MLP）上评估ADLA，该模型部署于ChipWhisperer-Husky评估平台。我们考虑了采用混洗和随机抖动抗攻击手段的保护实现。实验结果表明，与TVLA相比，ADLA在低迹线数量的保护实现中能提供更优的泄漏检测灵敏度。