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 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.
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Notes
- 1.
The authors mention they conducted a large number of experiments to find a reward function that works well for different datasets and leakage models, so we decided to use the same reward function.
- 2.
Many works consider the development of SCA countermeasures, but not specifically against deep learning approaches.
- 3.
The countermeasures set is an ordered set based on the order that the RL agent selected them. Since the countermeasures are applied in this order, sets with the same countermeasures but a different ordering are treated as disjoint.
- 4.
Note that the misleading GE behavior as discussed in [27] may happen during the experiments. Although one could reverse the ranking provided by an attack to obtain the correct key, we argue it is not possible in reality as an attacker would always assume the correct key being the one with the lowest GE (most likely guess).
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A Q-Learning Performance for the ASCAD with Random Keys Dataset
A Q-Learning Performance for the ASCAD with Random Keys Dataset
An overview of the Q-Learning performance for the ASCAD with the random keys dataset experiments. The blue line indicates the rolling average of the Q-Learning reward for 50 iterations, where at each iteration, we generate and evaluate a countermeasure set. The bars in the graph indicate the average Q-Learning reward for all countermeasure sets generated during that \(\varepsilon \). The results for RS experiments are similar. (Color figure online)
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Rijsdijk, J., Wu, L., Perin, G. (2022). Reinforcement Learning-Based Design of Side-Channel Countermeasures. In: Batina, L., Picek, S., Mondal, M. (eds) Security, Privacy, and Applied Cryptography Engineering. SPACE 2021. Lecture Notes in Computer Science(), vol 13162. Springer, Cham. https://doi.org/10.1007/978-3-030-95085-9_9
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