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Reinforcement Learning-Based Design of Side-Channel Countermeasures

Authors
  • Rijsdijk, Jorai (author)
  • Wu, L. (author)
  • Perin, G. (author)
Publication Date
Jan 01, 2022
Identifiers
DOI: 10.1007/978-3-030-95085-9_9
OAI: oai:tudelft.nl:uuid:f3b39261-b408-4a22-86bf-410dec7764eb
Source
TU Delft Repository
Keywords
Language
English
License
Unknown
External links

Abstract

<p>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 implementations. 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.</p> / Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. / Cyber Security

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