PCA, Eigenvector Localization and Clustering for Side-Channel Attacks on Cryptographic Hardware Devices

  • Dimitrios Mavroeidis
  • Lejla Batina
  • Twan van Laarhoven
  • Elena Marchiori
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7523)


Spectral methods, ranging from traditional Principal Components Analysis to modern Laplacian matrix factorization, have proven to be a valuable tool for a wide range of diverse data mining applications. Commonly these methods are stated as optimization problems and employ the extremal (maximal or minimal) eigenvectors of a certain input matrix for deriving the appropriate statistical inferences. Interestingly, recent studies have questioned this “modus operandi” and revealed that useful information may also be present within low-order eigenvectors whose mass is concentrated (localized) in a small part of their indexes. An application context where localized low-order eigenvectors have been successfully employed is “Differential Power Analysis” (DPA). DPA is a well studied side-channel attack on cryptographic hardware devices (such as smart cards) that employs statistical analysis of the device’s power consumption in order to retrieve the secret key of the cryptographic algorithm. In this work we propose a data mining (clustering) formulation of the DPA process and also provide a theoretical model that justifies and explains the utility of low-order eigenvectors. In our data mining formulation, we consider that the key-relevant information is modelled as a “low-signal” pattern that is embedded in a “high-noise” dataset. In this respect our results generalize beyond DPA and are applicable to analogous low-signal, hidden pattern problems. The experimental results using power trace measurements from a programmable smart card, verify our approach empirically.


Smart Card Cluster Structure Singular Vector Spectral Cluster Cluster Centroid 
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

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Dimitrios Mavroeidis
    • 1
  • Lejla Batina
    • 1
  • Twan van Laarhoven
    • 1
  • Elena Marchiori
    • 1
  1. 1.Institute for Computing and Information SciencesRadboud University NijmegenThe Netherlands

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