Efficient Stochastic Methods: Profiled Attacks Beyond 8 Bits

Abstract

Template attacks and stochastic models are among the most powerful side-channel attacks. However, they can be computationally expensive when processing a large number of samples. Various compression techniques have been used very successfully to reduce the data dimensionality prior to applying template attacks, most notably Principal Component Analysis (PCA) and Fisher’s Linear Discriminant Analysis (LDA). These make the attacks more efficient computationally and help the profiling phase to converge faster. We show how these ideas can also be applied to implement stochastic models more efficiently, and we also show that they can be applied and evaluated even for more than eight unknown data bits at once.