Trojan Playground: A Reinforcement Learning Framework for Hardware Trojan Insertion and Detection. (arXiv:2305.09592v1 [cs.CR])

Current Hardware Trojan (HT) detection techniques are mostly developed based
on a limited set of HT benchmarks. Existing HT benchmarks circuits are
generated with multiple shortcomings, i.e., i) they are heavily biased by the
designers' mindset when they are created, and ii) they are created through a
one-dimensional lens, mainly the signal activity of nets. To address these
shortcomings, we introduce the first automated reinforcement learning (RL) HT
insertion and detection framework. In the insertion phase, an RL agent explores …

benchmarks current designers detection framework generated hardware mindset techniques trojan