Robust Profiling for DPA-Style Attacks

Carolyn Whitnall, Elisabeth Oswald

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

29 Citations (Scopus)

Abstract

Profiled side-channel attacks are understood to be powerful when applicable: in the best case when an adversary can comprehensively characterise the leakage, the resulting model leads to attacks requiring a minimal number of leakage traces for success. Such ‘complete’ leakage models are designed to capture the scale, location and shape of the profiling traces, so that any deviation between these and the attack traces potentially produces a mismatch which renders the model unfit for purpose. This severely limits the applicability of profiled attacks in practice and so poses an interesting research challenge: how can we design profiled distinguishers that can tolerate (some) differences between profiling and attack traces?

This submission is the first to tackle the problem head on: we propose distinguishers (utilising unsupervised machine learning methods, but also a ‘down-to-earth’ method combining mean traces and PCA) and evaluate their behaviour across an extensive set of distortions that we apply to representative trace data. Our results show that the profiled distinguishers are effective and robust to distortions to a surprising extent.
Original languageEnglish
Title of host publicationCryptographic Hardware and Embedded Systems - CHES 2015
EditorsTim Güneysu, Helena Handschuh
PublisherSpringer
Pages3-21
Number of pages18
Volume9293
ISBN (Electronic) 9783662483244
ISBN (Print) 9783662483237
DOIs
Publication statusPublished - 1 Sep 2015

Publication series

NameLecture Notes in Computer Science

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