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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.
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 language | English |
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Title of host publication | Cryptographic Hardware and Embedded Systems - CHES 2015 |
Editors | Tim Güneysu, Helena Handschuh |
Publisher | Springer |
Pages | 3-21 |
Number of pages | 18 |
Volume | 9293 |
ISBN (Electronic) | 9783662483244 |
ISBN (Print) | 9783662483237 |
DOIs | |
Publication status | Published - 1 Sep 2015 |
Publication series
Name | Lecture Notes in Computer Science |
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Dive into the research topics of 'Robust Profiling for DPA-Style Attacks'. Together they form a unique fingerprint.Projects
- 1 Finished
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SILENT: Rework of Side channels-theory and implications for society
Oswald, M. E.
1/01/11 → 1/04/16
Project: Research