An Exploration of the Kolmogorov-Smirnov Test as a Competitor to Mutual Information Analysis

A theme of recent side-channel research has been the quest for distinguishers which remain e ective even when few assumptions can be made about the underlying distribution of the measured leakage traces. The Kolmogorov-Smirnov (KS) test is a well known nonparametric method for distinguishing between distributions, and, as such, a perfect candidate and an interesting competitor to the (already much discussed) mutual information (MI) based attacks. However, the sidechannel distinguisher based on the KS test statistic has received only cursory evaluation so far, which is the gap we narrow here. This contribution explores the e ectiveness and e ciency of Kolmogorov-Smirnov analysis (KSA), and compares it with mutual information analysis (MIA) in a number of relevant scenarios ranging from optimistic rst-order DPA to multivariate settings. We show that KSA shares certain `generic' capabilities in common with MIA whilst being more robust to noise than MIA in univariate settings. This has the practical implication that designers should consider results of KSA to determine the resilience of their designs against univariate power analysis attacks.