Reinforcement Learning-Based Design of Side-Channel Countermeasures

Reinforcement Learning-Based Design of Side-Channel Countermeasures

Rijsdijk, Jorai (Student TU Delft)
Wu, L. (TU Delft Cyber Security)
Perin, G. (TU Delft Cyber Security)

Batina, Lejla (editor)
Picek, Stjepan (editor)
Picek, Stjepan (editor)
Mondal, Mainack (editor)

2022

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.

Countermeasures
Deep learning
Reinforcement learning
Side-channel analysis

http://resolver.tudelft.nl/uuid:f3b39261-b408-4a22-86bf-410dec7764eb

https://doi.org/10.1007/978-3-030-95085-9_9

Springer, Cham

2022-07-01

978-3-030-95084-2

Security, Privacy, and Applied Cryptography Engineering: 11th International Conference, SPACE 2021, Proceedings (1)

11th International Conference on Security, Privacy, and Applied Cryptography Engineering, SPACE 2021, 2021-12-10 → 2021-12-13, Virtual, Online at Kolkata, India

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 0302-9743, 13162

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.

Institutional Repository

conference paper

© 2022 Jorai Rijsdijk, L. Wu, G. Perin