  As the semiconductor industry has shifted to a fabless paradigm, the risk of
hardware Trojans being inserted at various stages of production has also
increased. Recently, there has been a growing trend toward the use of machine
learning solutions to detect hardware Trojans more effectively, with a focus on
the accuracy of the model as an evaluation metric. However, in a high-risk and
sensitive domain, we cannot accept even a small misclassification.
Additionally, it is unrealistic to expect an ideal model, especially when
Trojans evolve over time. Therefore, we need metrics to assess the
trustworthiness of detected Trojans and a mechanism to simulate unseen ones. In
this paper, we generate evolving hardware Trojans using our proposed novel
conformalized generative adversarial networks and offer an efficient approach
to detecting them based on a non-invasive algorithm-agnostic statistical
inference framework that leverages the Mondrian conformal predictor. The method
acts like a wrapper over any of the machine learning models and produces set
predictions along with uncertainty quantification for each new detected Trojan
for more robust decision-making. In the case of a NULL set, a novel method to
reject the decision by providing a calibrated explainability is discussed. The
proposed approach has been validated on both synthetic and real chip-level
benchmarks and proven to pave the way for researchers looking to find informed
machine learning solutions to hardware security problems.
