Reinforcement Learning for Hardware Security: Opportunities, Developments, and Challenges. (arXiv:2208.13885v1 [cs.CR])

Reinforcement learning (RL) is a machine learning paradigm where an
autonomous agent learns to make an optimal sequence of decisions by interacting
with the underlying environment. The promise demonstrated by RL-guided
workflows in unraveling electronic design automation problems has encouraged
hardware security researchers to utilize autonomous RL agents in solving
domain-specific problems. From the perspective of hardware security, such
autonomous agents are appealing as they can generate optimal actions in an
unknown adversarial environment. On the other hand, the continued …

challenges hardware hardware security security