  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
the circuits and finds different locations that are best for keeping inserted
HTs hidden. On the defense side, we introduce a multi-criteria RL-based
detector that generates test vectors to discover the existence of HTs. Using
the proposed framework, one can explore the HT insertion and detection design
spaces to break the human mindset limitations as well as the benchmark issues,
ultimately leading toward the next-generation of innovative detectors. Our HT
toolset is open-source to accelerate research in this field and reduce the
initial setup time for newcomers. We demonstrate the efficacy of our framework
on ISCAS-85 benchmarks and provide the attack and detection success rates and
define a methodology for comparing our techniques.

Cryptography and Security;Hardware Architecture