Improved Blind Side-Channel Analysis by Exploitation of Joint Distributions of Leakages
Classical side-channel analysis include statistical attacks which require the knowledge of either the plaintext or the ciphertext to predict some internal value to be correlated to the observed leakages.In this paper we revisit a blind (i.e. leakage-only) attack from Linge et al. that exploits joint distributions of leakages. We show – both by simulations and concrete experiments on a real device – that the maximum likelihood (ML) approach is more efficient than Linge’s distance-based comparison of distributions, and demonstrate that this method can be easily adapted to deal with implementations protected by first-order Boolean masking. We give example applications of different variants of this approach, and propose countermeasures that could prevent them.Interestingly, we also observe that, when the inputs are known, the ML criterion is more efficient than correlation power analysis.