IACR News item: 20 January 2015
Valentina Banciu, Elisabeth Oswald, Carolyn Whitnall
ePrint Reportrely on the capability of reliably extracting side-channel
information (e.g. Hamming weights of intermediate target values)
from traces. In particular, in original versions of simple power
analysis (SPA) or algebraic side channel attacks (ASCA) it was
assumed that an adversary can correctly extract the Hamming
weight values for all the intermediates used in an attack. Recent
developments in error tolerant SPA style attacks relax this
unrealistic requirement on the information extraction and bring
renewed interest to the topic of template building or training
suitable machine learning classifiers.
In this work we ask which classifiers or methods, if any, are
most likely to return the true Hamming weight among their first
(say $s$) ranked outputs. We experiment on two data sets with
different leakage characteristics. Our experiments show that the
most suitable classifiers to reach the required performance for
pragmatic SPA attacks are Gaussian templates, Support Vector
Machines and Random Forests, across the two data sets that we
considered. We found no configuration that was able to satisfy
the requirements of an error tolerant ASCA in case of complex
leakage.
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