Challenging Assumptions of Normality in AES s-Box Configurations Under Side-Channel Analysis -- Experimental Data

Article Abstract:Power-based Side-Channel Analysis (SCA) began with visual-based examinations and has progressed to utilize data-driven statistical analysis. Two distinct classifications of these methods have emerged over the years; those focused on leakage exploitation and those dedicated to leakage detection. This work primarily focuses on a leakage detection-based schema that utilizes Welch's t-test, known as Test Vector Leakage Assessment (TVLA). Both classes of methods process collected data using statistical frameworks that result in the successful exfiltration of information via SCA. Often, statistical testing used during analysis requires the assumption that collected power consumption data originate from a normal distribution. To date, this assumption has remained largely uncontested. This work seeks to demonstrate that while past studies have assumed the normality of collected power traces, this assumption should be properly evaluated. In order to evaluate this assumption, an implementation of Tiny-AES-c with nine unique substitution-box (s-box) configurations is conducted using TVLA to guide experimental design. By leveraging the complexity of the AES algorithm, a sufficiently diverse and complex dataset was developed. Under this dataset, statistical tests for normality such as the Shapiro-Wilk test and the Kolmogorov-Smirnov test provide significant evidence to reject the null hypothesis that the power consumption data are normally distributed. To address this observation, existing non-parametric equivalents such as the Wilcoxon Signed-Rank Test and the Kruskal-Wallis Test are discussed in relation to currently used parametric tests such as Welch's t-test.论文摘要：基于功耗的边信道分析（Side-Channel Analysis, SCA）初期采用基于视觉的检测手段，如今已演进为依托数据驱动的统计分析方法。多年来，该领域已形成两类截然不同的分析路径：一类聚焦于泄露利用，另一类则专门针对泄露检测。本研究主要聚焦于一类基于泄露检测的分析框架，该框架采用Welch t检验（Welch's t-test），又称测试向量泄露评估（Test Vector Leakage Assessment, TVLA）。两类方法均通过统计框架处理采集所得数据，最终可通过边信道分析成功提取敏感信息。 分析过程中常用的统计检验通常默认假设采集到的功耗数据服从正态分布，这一假设迄今基本未受到质疑。本研究旨在证明：尽管过往研究均默认采集的功耗轨迹服从正态分布，但该假设应当经过严格验证。 为验证该假设，本研究以测试向量泄露评估（TVLA）指导实验设计，针对搭载9种独特替换盒（substitution-box, s-box）配置的Tiny-AES-c实现展开测试。借助高级加密标准（Advanced Encryption Standard, AES）算法的复杂性，本研究构建了具备足够多样性与复杂度的数据集。基于该数据集，夏皮罗-威尔克检验（Shapiro-Wilk test）与柯尔莫哥洛夫-斯米尔诺夫检验（Kolmogorov-Smirnov test）等正态性统计检验均提供了充分证据，足以拒绝“功耗数据服从正态分布”的原假设。 针对这一发现，本研究将当前常用的参数化检验（如Welch t检验）与现有非参数等效检验（如威尔科克森符号秩检验（Wilcoxon Signed-Rank Test）、克鲁斯卡尔-沃利斯检验（Kruskal-Wallis Test））展开对比讨论。