An Exploration of the Kolmogorov-Smirnov Test as a Competitor to Mutual Information Analysis

Carolyn A Whitnall, M E Oswald, Luke T Mather

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

34 Citations (Scopus)
291 Downloads (Pure)

Abstract

A theme of recent side-channel research has been the quest for distinguishers which remain effective even when few assumptions can be made about the underlying distribution of the measured leakage traces. The Kolmogorov-Smirnov (KS) test is a well known non-parametric method for distinguishing between distributions, and, as such, a perfect candidate and an interesting competitor to the (already much discussed) mutual information (MI) based attacks. However, the side-channel distinguisher based on the KS test statistic has received only cursory evaluation so far, which is the gap we narrow here. This contribution explores the effectiveness and efficiency of Kolmogorov-Smirnov analysis (KSA), and compares it with mutual information analysis (MIA) in a number of relevant scenarios ranging from optimistic first-order DPA to multivariate settings. We show that KSA shares certain ‘generic’ capabilities in common with MIA whilst being more robust to noise than MIA in univariate settings. This has the practical implication that designers should consider results of KSA to determine the resilience of their designs against univariate power analysis attacks.
Original languageEnglish
Title of host publicationSmart Card Research and Advanced Applications
Subtitle of host publication10th IFIP WG 8.8/11.2 International Conference, CARDIS 2011, Leuven, Belgium, September 14-16, 2011, Revised Selected Papers
EditorsEmmanuel Prouff
PublisherSpringer Berlin Heidelberg
Pages234-251
Number of pages18
ISBN (Electronic)9783642272578
ISBN (Print)9783642272561
DOIs
Publication statusPublished - 2011

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Verlag
Volume7079
ISSN (Print)0302-9743

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