Towards Micro-architectural Leakage Simulators: Reverse Engineering Micro-architectural Leakage Features Is Practical

Daniel Page, M E Oswald, Si Gao

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

1 Citation (Scopus)

Abstract

Leakage simulators offer the tantalising promise of easy and quick testing of software with respect to the presence of side channel leakage. The quality of their build in leakage models is therefore crucial, this includes the faithful inclusion of micro-architectural leakage. Micro-architectural leakage is a reality even on low- to mid-range commercial processors, such as the ARM Cortex M series. Dealing with it seems initially infeasible in a “grey box” setting: how should we describe it if micro-architectural elements are not publicly known?

We demonstrate, for the first time, that it is feasible, using a recent leakage modelling technique, to reverse engineer significant elements of the micro-architectural leakage of a commercial processor. Our approach first recovers the micro-architectural leakage of each stage in the pipeline, and the leakage of elements that are known to produce glitches. Using the reverse engineered leakage features we build an enhanced version of the popular leakage simulator ELMO.
Original languageEnglish
Title of host publicationAdvances in Cryptology – EUROCRYPT 2022 - 41st Annual International Conference on the Theory and Applications of Cryptographic Techniques, 2022, Proceedings
EditorsOrr Dunkelman, Stefan Dziembowski
PublisherSpringer Verlag
Pages284-311
Number of pages28
VolumeLNCS 13277
ISBN (Print)9783031070815
DOIs
Publication statusPublished - 25 May 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13277 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Bibliographical note

Funding Information:
Acknowledments. We would like to thank Ben Marshall for his invaluable insights, which guided us through various mazes in our leakage modelling efforts. Si Gao and Elisabeth Oswald were funded in part by the ERC via the grant SEAL (Project Reference 725042). This work has been supported in part by EPSRC via grant EP/R012288/1, under the RISE (http://www.ukrise.org) programme.

Publisher Copyright:
© 2022, International Association for Cryptologic Research.

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