Formal Analysis of Non-profiled Deep-learning Based Side-channel Attacks

ePrint Report: Formal Analysis of Non-profiled Deep-learning Based Side-channel Attacks

Akira Ito, Rei Ueno, Rikuma Tanaka, Naofumi Homma

This paper formally analyzes two major non-profiled deep-learning-based side-channel attacks (DL-SCAs): differential deep-learning analysis (DDLA) by Timon and collision DL-SCA by Staib and Moradi. These DL-SCAs leverage supervised learning in non-profiled scenarios. Although some intuitive descriptions of these DL-SCAs exist, their formal analyses have been rarely conducted yet, which makes it unclear why and when the attacks succeed and how the attack …

akira analysis attacks channel collision eprint report major non report sca side-channel side-channel attacks