Profiling Resilient to Change in Probe Position

TL;DR

This paper presents a neural network approach trained on EM side channel traces from multiple probe positions, enabling more resilient profiling across different physical setups and labs.

Contribution

It introduces a method for training a single neural network on diverse EM traces to improve attack robustness across probe positions and different labs.

Findings

Neural network trained on multi-position EM traces enhances leakage detection.

Cross-lab evaluation shows effective profiling and attack transferability.

Method improves resilience to probe repositioning in EM side channel analysis.

Abstract

Side Channel Analysis (SCA) relaxes the black-box assumption of conventional cryptanalysis by incorporating physical measurements acquired during cryptographic operations. Electro-magnetic (EM) emissions of a chip during computations often provide a very valuable source of side channel leakage. During the evaluation of a chip for electro-magnetic side channel emissions one needs to position an electro-magnetic probe in an advantageous position relative to the chip. Previous literature focused on hot-spot finding and to a lower extend repositioning. Trace augmentations have been considered to aid portability of profiling using one physical device and attacking another device. This paper focuses on training a single neural network using traces from multiple EM probe positions to detect leakage from a larger area over the attacked device. We provide dual evaluation of EM traces - from two completely independent labs - profiling on data from one lab and attacking traces from the other lab.