Auto-tune POIs: Estimation of distribution algorithms for efficient side-channel analysis

TL;DR

This paper introduces the use of Estimation of Distribution Algorithms to automate the selection of points of interest in side-channel analysis, simplifying and improving the efficiency of profiling attacks on IoT devices.

Contribution

It presents a novel application of EDAs for automatic POI tuning in SCA, reducing complexity and enhancing attack effectiveness on various AES implementations.

Findings

EDAs effectively automate POI selection in SCA.

The approach works on both protected and unprotected AES devices.

Experimental results show improved attack efficiency.

Abstract

Due to the constant increase and versatility of IoT devices that should keep sensitive information private, Side-Channel Analysis (SCA) attacks on embedded devices are gaining visibility in the industrial field. The integration and validation of countermeasures against SCA can be an expensive and cumbersome process, especially for the less experienced ones, and current certification procedures require to attack the devices under test using multiple SCA techniques and attack vectors, often implying a high degree of complexity. The goal of this paper is to ease one of the most crucial and tedious steps of profiling attacks i.e. the points of interest (POI) selection and hence assist the SCA evaluation process. To this end, we introduce the usage of Estimation of Distribution Algorithms (EDAs) in the SCA field in order to automatically tune the point of interest selection. We showcase our approach on several experimental use cases, including attacks on unprotected and protected AES implementations over distinct copies of the same device, dismissing in this way the portability issue.