Climbing Down the Hierarchy: Hierarchical Classification for Machine Learning Side-Channel Attacks (CROSBI ID 648188)
Prilog sa skupa u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Picek, Stjepan ; Heuser, Annelie ; Jović, Alan ; Legay, Axel
engleski
Climbing Down the Hierarchy: Hierarchical Classification for Machine Learning Side-Channel Attacks
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 all the 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.
Side-channel attacks ; profiled scenario ; machine learning techniques ; hierarchical classification ; hierarchical attack ; structured attack
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Podaci o prilogu
61-78.
2017.
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objavljeno
Podaci o matičnoj publikaciji
Lecture notes in computer science
Joye, M. ; Nitaj A.
Cham: Springer
978-3-319-57338-0
0302-9743
Podaci o skupu
9th International Conference on Cryptology in Africa
predavanje
24.05.2017-26.05.2017
Dakar, Senegal