First Principal Components Analysis: A New Side Channel Distinguisher

Abstract

Side Channel Analysis (SCA) are of great concern since they have shown their efficiency in retrieving sensitive information from secure devices. In this paper we introduce First Principal Components Analysis (FPCA) which consists in evaluating the relevance of a partitioning using the projection on the first principal directions as a distinguisher. Indeed, FPCA is a novel application of the Principal Component Analysis (PCA). In SCA like Template attacks, PCA has been previously used as a pre-processing tool. The originality of FPCA is to use PCA no more as a preprocessing tool but as a distinguisher. We conducted all our experiments in real life context, using a recently introduced practiceoriented SCA evaluation framework. We show that FPCA is more performant than first-order SCA (DoM, DPA, CPA) when performed on unprotected DES architecture. Moreover, we outline that FPCA is still efficient on masked DES implementation, and show how it outperforms Variance Power Analysis (VPA) which is a known successful attack on such countermeasures.