Side channel attacks on cryptographic devices as a classification problem

In this contribution we examine three data reduction techniques in the context of Template Attacks. The Template Attack is a powerful two-step side channel attack which models an almost omnipotent adversary in the profiling step, but restricts him to a single observation in the classification step. The profiling step requires data reduction due to computational complexity and vast amounts of data. Here we examine the inter class variance, the Spearman correlation coefficient, and principal component analysis. The classification step requires a distinguisher, which we implemented by linear discriminant analysis. Our results lead to the conclusion that PCA in combination with LDA gives the highest classification accuracies on unseen data from the tried linear classifier methods.