  Current Hardware Trojan (HT) detection techniques are mostly developed based
on a limited set of HT benchmarks. Existing HT benchmark circuits are generated
with multiple shortcomings, i.e., i) they are heavily biased by the designers'
mindset when created, and ii) they are created through a one-dimensional lens,
mainly the signal activity of nets. We introduce the first automated
Reinforcement Learning (RL) HT insertion and detection framework to address
these shortcomings. In the HT insertion phase, an RL agent explores the
circuits and finds locations best for keeping inserted HTs hidden. On the
defense side, we introduce a multi-criteria RL-based HT 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
limitations of human mindset and benchmark issues, ultimately leading toward
the next generation of innovative detectors. We demonstrate the efficacy of our
framework on ISCAS-85 benchmarks, provide the attack and detection success
rates, and define a methodology for comparing our techniques.
