Semi-Supervised template attack

Authors:
Liran Lerman;Stephane Fernandes Medeiros;Nikita Veshchikov;Cédric Meuter;Gianluca Bontempi;Olivier Markowitch
Affiliations:
Quality and security of Information Systems, Département d'informatique, Université Libre de Bruxelles, Belgium,Machine Learning Group, Département d'informatique, Université L ...;Quality and security of Information Systems, Département d'informatique, Université Libre de Bruxelles, Belgium;Quality and security of Information Systems, Département d'informatique, Université Libre de Bruxelles, Belgium;Atos Worldline, Belgium;Machine Learning Group, Département d'informatique, Université Libre de Bruxelles, Belgium;Quality and security of Information Systems, Département d'informatique, Université Libre de Bruxelles, Belgium
Venue:
COSADE'13 Proceedings of the 4th international conference on Constructive Side-Channel Analysis and Secure Design
Year:
2013

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Abstract

Side channel attacks take advantage of information leakages in cryptographic devices. Template attacks form a family of side channel attacks which is reputed to be extremely effective. This kind of attacks assumes that the attacker fully controls a cryptographic device before attacking a similar one. In this paper, we propose to relax this assumption by generalizing the template attack using a method based on a semi-supervised learning strategy. The effectiveness of our proposal is confirmed by software simulations, by experiments on a 8-bit microcontroller and by a comparison to a template attack as well as to two supervised machine learning methods.