Hardware Trojans (HTs) are undesired design or manufacturing modifications
that can severely alter the security and functionality of digital integrated
circuits. HTs can be inserted according to various design criteria, e.g., nets
switching activity, observability, controllability, etc. However, to our
knowledge, most HT detection methods are only based on a single criterion,
i.e., nets switching activity. This paper proposes a multi-criteria
reinforcement learning (RL) HT detection tool that features a tunable reward
function for different HT detection scenarios. The tool allows for exploring
existing detection strategies and can adapt new detection scenarios with
minimal effort. We also propose a generic methodology for comparing HT
detection methods fairly. Our preliminary results show an average of 84.2%
successful HT detection in ISCAS-85 benchmark

该论文提出了一种基于多标准强化学习方法的硬件特洛伊检测工具，能有效地探索现有的检测策略和适应新的检测场景，并且提供了一种公正的比较硬件特洛伊检测方法的方法。该工具在 ISCAS-85 基准测试中成功检测率平均达到了 84.2%。