Profiling Resilient to Change in Probe Position
2026-04-27 • Cryptography and Security
Cryptography and Security
AI summaryⓘ
The authors study a way to improve side channel analysis, which is a method to find hidden information in devices by measuring things like electromagnetic (EM) signals during their operation. Instead of using EM signals from just one spot on a chip, they train a neural network using data from multiple probe positions to cover a larger area and better detect leaks. They tested their approach by training on data from one lab and successfully attacking data collected independently in another lab. This shows their method could work well across different setups.
Side Channel AnalysisElectro-magnetic EmissionsNeural NetworkProfiling AttackCryptanalysisProbe PositioningTrace AugmentationLeakage Detection
Authors
Elie Bursztein, Michael Gruber, Karel Král, Jean-Michel Picod, Matthias Probst, Georg Sigl
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.