You are here

[PhD Public defense] A Machine Learning Approach for Automatic and Generic Side-Channel Attacks

Public defense of the PhD thesis of Liran Lerman entitled "A Machine Learning Approach for Automatic and Generic Side-Channel Attacks" and supervised by Prof. Gianluca Bontempi and Prof. Olivier Markowitch. The defence (in french) will take place at the Université libre de Bruxelles (in the La Plaine, N-O building, 5th floor, Salle Solvay) on Wednesday, June 10, at 16:30. Abstract: The pervasive presence of interconnected devices has lead to a massive interest in security features provided among others by cryptography. For decades, designers estimated the security level of a cryptographic algorithm independently of its implementation in a cryptographic device viagra for sale uk. However, since the publication on implementation attacks in 1996, the physical attacks have become an active research area by analysing physical leakages measured on the target cryptographic device. In this work, we focus on profiled attacks that were introduced as the most efficient class of strategies in order to evaluate the physical leakages of cryptographic devices. The traditional profiled attacks apply parametric methods in which a priori information on the physical properties is assumed. Machine learning represents an active research area that allows producing automatic and generic models requiring no a priori information on the underlying phenomenon. The purpose of this presentation is to shed new light on the capabilities of learning models for physical analysis.

Theme by Danetsoft and Danang Probo Sayekti inspired by Maksimer