Trojan Playground: A Reinforcement Learning Framework for Hardware Trojan Insertion and Detection

arXiv:2305.09592v2 Announce Type: replace
Abstract: 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, …

arxiv benchmark benchmarks cs.ar cs.cr current designers detection framework generated hardware mindset techniques trojan