Gaussian mixture models for higher-order side channel analysis

Abstract

We introduce the use of multivariate Gaussian mixture models for enhancing higher-order side channel analysis on masked cryptographic implementations. Our contribution considers an adversary with incomplete knowledge at profiling, i.e., the adversary does not know random numbers used for masking. At profiling, the adversary observes a mixture probability density of the side channel leakage. However, the EM algorithm can provide estimates on the unknown parameters of the component densities using samples drawn from the mixture density. Practical results are presented and confirm the usefulness of Gaussian mixture models and the EM algorithm. Especially, success rates obtained by automatic classification based on the estimates of the EM algorithm are very close to success rates of template attacks. © Springer-Verlag Berlin Heidelberg 2007.