Side channel attack: An approach based on machine learning

Abstract. In cryptography, a side channel attack is any attack based on the analysis of measurements related to the physical implementa-tion of a cryptosystem. Nowadays, the possibility of collecting a large amount of observations paves the way to the adoption of machine learn-ing techniques, i.e. techniques able to extract information and patterns from large datasets. The use of statistical techniques for side channel at-tacks is not new. Techniques like Template Based DPA have shown their effectiveness in recent years. However these techniques rely on paramet-ric 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 re-sults regarding the 3DES encryption scheme confirms the importance of adopting machine learning approaches in cryptanalysis