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

  • Carolyn Whitnall
  • Elisabeth Oswald
  • Luke Mather
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7079)


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.


Trace Requirement Distinguishing Vector Template Attack Mutual Information Analysis Independent Gaussian Noise 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© IFIP International Federation for Information Processing 2011

Authors and Affiliations

  • Carolyn Whitnall
    • 1
  • Elisabeth Oswald
    • 1
  • Luke Mather
    • 1
  1. 1.Department of Computer ScienceUniversity of BristolBristolUK

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