Paper 2021/526

Reinforcement Learning-based Design of Side-channel Countermeasures

Jorai Rijsdijk, Lichao Wu, and Guilherme Perin

Abstract

Deep learning-based side-channel attacks are capable of breaking targets protected with countermeasures. The constant progress in the last few years makes the attacks more powerful, requiring fewer traces to break a target. Unfortunately, to protect against such attacks, we still rely solely on methods developed to protect against generic attacks. The works considering the protection perspective are few and usually based on the adversarial examples concepts, which are not always easy to translate to real-world hardware implementation. In this work, we ask whether we can develop combinations of countermeasures that protect against side-channel attacks. We consider several widely adopted hiding countermeasures and use the reinforcement learning paradigm to design specific countermeasures that show resilience against deep learning-based side-channel attacks. Our results show that it is possible to significantly enhance the target resilience to a point where deep learning-based attacks cannot obtain secret information. At the same time, we consider the cost of implementing such countermeasures to balance security and implementation costs. The optimal countermeasure combinations can serve as development guidelines for real-world hardware/software-based protection schemes.

Metadata
Available format(s)
PDF
Category
Implementation
Publication info
Preprint. MINOR revision.
Keywords
Side-channel analysisReinforcement learningCountermeasuresDeep learning
Contact author(s)
jorai @ jrijsdijk com
lichao wu9 @ gmail com
guilhermeperin7 @ gmail com
History
2021-09-28: revised
2021-04-23: received
See all versions
Short URL
https://ia.cr/2021/526
License
Creative Commons Attribution
CC BY

BibTeX

@misc{cryptoeprint:2021/526,
      author = {Jorai Rijsdijk and Lichao Wu and Guilherme Perin},
      title = {Reinforcement Learning-based Design of Side-channel Countermeasures},
      howpublished = {Cryptology ePrint Archive, Paper 2021/526},
      year = {2021},
      note = {\url{https://eprint.iacr.org/2021/526}},
      url = {https://eprint.iacr.org/2021/526}
}
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