Attacks Beyond 8 Bits
Omar Choudary, Markus Kühn · 2015
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 compres- sion techniques have been used very successfully to reduce the data di- mensionality prior to applying template attacks, most notably Principal Component Analysis (PCA) and Fisher's Linear Discriminant Analysis (LDA). These make the attacks more ecient computationally and help the proling phase to converge faster. We show how these ideas can also be applied to implement stochastic models more eciently, and we also show that they can be applied and evaluated even for more than eight unknown data bits at once.