Lightweight Ciphers and their Side-channel Resilience

Annelie Heuser, Stjepan Picek, Sylvain Guilley, Nele Mentens

Research output: Contribution to journalArticleScientificpeer-review

16 Citations (Scopus)
277 Downloads (Pure)

Abstract

Side-channel attacks represent a powerful category of attacks against cryptographic devices. Still, side-channel analysis for lightweight ciphers is much less investigated than for instance for AES. Although intuition may lead to the conclusion that lightweight ciphers are weaker in terms of side-channel resistance, that remains to be confirmed and quantified. In this paper, we consider various side-channel analysis metrics which should provide an insight on the resistance of lightweight ciphers against side-channel attacks. In particular, for the non-profiled scenario we use the theoretical confusion coefficient and empirical optimal distinguisher. Our study considers side-channel attacks on the first, the last, or both rounds simultaneously. Furthermore, we conduct a profiled side-channel analysis using various machine learning attacks to recover 4-bit and 8-bit intermediate states of the cipher. Our results show that the difference between AES and lightweight ciphers is smaller than one would expect, and even find scenarios in which lightweight ciphers may be more resistant. Interestingly, we observe that the studied 4-bit S-boxes have a different side-channel resilience, while the difference in the 8-bit ones is only theoretically present.

Original languageEnglish
Article number8053814
Pages (from-to)1434-1448
Number of pages15
JournalIEEE Transactions on Computers
Volume69
Issue number10
DOIs
Publication statusPublished - 1 Oct 2020

Bibliographical note

Accepted Author Manuscript
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.

Keywords

  • Side-channel analysis
  • lightweight ciphers
  • optimal distinguisher
  • confusion coefficient
  • success rate
  • machine learning attacks

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