Side-channel analysis (SCA) is usually used for security evaluation to test the side-channel vulnerability of a cryptographic device. However, in practice, an analyser may need to cope with enormous amounts of side-channel measurement data to extract valuable information for SCA. Under the circumstances, side-channel leakage detection can be used to identify leakage points which contain secret information and therefore improve the efficiency of security assessment. This investigation proposes a new black-box leakage detection approach on the basis of the one-way analysis of variance (ANOVA). In accordance with the relevance between leakage points and inputs of a cryptographic algorithm, the proposed method divides side-channel samples into multiple classes and tests the difference among these classes by taking advantage of the one-way ANOVA. Afterwards, leakage points and nonleakage points can be distinguished by determining whether the null hypothesis is accepted. Further, we extend our proposed method to multichannel leakage detection. In particular, a new SCA attack with a -statistic-based distinguisher is capable of developing if the input of the leakage detection approach is replaced by a sensitive intermediate variable. Practical experiments show the effectiveness of the proposed methods.

中文翻译：

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单向方差分析的旁通道泄漏检测

辅助信道分析（SCA）通常用于安全评估，以测试密码设备的辅助信道漏洞。但是，在实践中，分析仪可能需要处理大量的副信道测量数据，以提取SCA的有价值的信息。在这种情况下，可以使用边信道泄漏检测来识别包含秘密信息的泄漏点，从而提高安全评估的效率。这项研究提出了一种新的黑匣子泄漏检测方法，该方法基于方差单向分析（ANOVA）。根据泄漏点与密码算法输入之间的相关性，提出的方法将边路样本分为多个类别，并利用单向方差分析来测试这些类别之间的差异。之后，可以通过确定是否接受零假设来区分泄漏点和非泄漏点。此外，我们将我们提出的方法扩展到多通道泄漏检测。特别是，新的SCA攻击具有-如果泄漏检测方法的输入被敏感的中间变量替代，则基于统计的区分器将能够开发。实际实验证明了所提方法的有效性。