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

Autori
Picek, Stjepan ; Heuser, Annelie ; Jović, Alan ; Legay, Axel

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Progress in Cryptology - AFRICACRYPT 2017, Lecture Notes in Computer Science (LNCS), vol. 10239 / Joye, M. ; Nitaj A. - Cham : Springer, 2017, 61-78

ISBN
978-3-319-57338-0

Skup
9th International Conference on Cryptology in Africa

Mjesto i datum
Dakar, Senegal, 24.05.2017. - 26.05.2017

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Side-channel attacks ; profiled scenario ; machine learning techniques ; hierarchical classification ; hierarchical attack ; structured attack

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA