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Side channel attack: an approach based on machine learning

TitleSide channel attack: an approach based on machine learning
Publication TypeBook Chapter
Year of Publication2011
AuthorsLerman, L, Bontempi, G, Markowitch, O
Book TitleSecond International Workshop on Constructive SideChannel Analysis and Secure Design
Pagination29–41
PublisherCenter for Advanced Security Research Darmstadt
Abstract

In cryptography, a side channel attack is any attack based on the analysis of measurements related to the physical implementation of a cryptosystem. Nowadays, the possibility of collecting a large amount of observations paves the way to the adoption of machine learning techniques, i.e. techniques able to extract information and patterns from large datasets. The use of statistical techniques for side channel attacks is not new. Techniques like Template Based DPA have shown their effectiveness in recent years. However these techniques rely on parametric assumptions and are often limited to small dimensionality setting, which limits their range of application. This paper explores the use of machine learning techniques to relax such assumption and to deal with high dimensional feature vectors.

For this purpose, we first formalize the problem of studying the relation between power consumption and encryption key as a supervised learning task. Then we compare and assess several classifiers and dimensionality reduction techniques in a real experimental setting. Our promising results regarding the 3DES encryption scheme confirms the importance of adopting machine learning approaches in cryptanalysis.

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