Reinforcement Learning-Based Design of Side-Channel Countermeasures

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.