Climbing Down the Hierarchy: Hierarchical Classification for Machine Learning Side-Channel Attacks

Stjepan Picek (1) , Annelie Heuser (2) , Alan Jovic (3) , Axel Legay (2)

1 UCL - Université Catholique de Louvain = Catholic University of Louvain
2 TAMIS - Threat Analysis and Mitigation for Information Security
3 University of Zagreb

Machine learning techniques represent a powerful paradigm in side-channel analysis, but they come with a price. Selecting the appropriate algorithm as well as the parameters can sometimes be a difficult task. Nevertheless, the results obtained usually justify such an effort. However, a large part of those results use simplification of the data relation and in fact do not consider allthe available information. In this paper, we analyze the hierarchical relation between the data and propose a novel hierarchical classification approach for side-channel analysis. With this technique, we are able to introduce two new attacks for machine learning side-channel analysis: Hierarchical attack and Structured attack. Our results show that both attacks can outperform machine learning techniques using the traditional approach as well as the template attack regarding accuracy. To support our claims, we give extensive experimental results and discuss the necessary conditions to conduct such attacks.

Domaines: Cryptographie et sécurité [cs.CR]

Stjepan Picek, Annelie Heuser, Alan Jovic, Axel Legay. Climbing Down the Hierarchy: Hierarchical Classification for Machine Learning Side-Channel Attacks. AFRICACRYPT 2017 - International Conference on Cryptology in Africa, May 2017, Dakar, Senegal. pp.61-78, ⟨10.1007/978-3-319-57339-7_4⟩. ⟨hal-01629878⟩