International Association for Cryptologic Research

International Association
for Cryptologic Research

IACR News item: 20 January 2015

Valentina Banciu, Elisabeth Oswald, Carolyn Whitnall
ePrint Report ePrint Report
Side-channel attacks using only a single trace crucially

rely 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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