Side-Channel Leakage Detection with One-Way Analysis of Variance

Abstract

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 $F$ -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.