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.
↧