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

翻译：硬件木马（HT）是对数字集成电路安全性与功能性造成严重破坏的不良设计或制造修改。硬件木马可根据多种设计准则（如网表切换活动、可观测性、可控性等）进行植入。然而，据我们所知，现有大多数HT检测方法仅基于单一准则（即网表切换活动）。本文提出一种基于多准则强化学习（RL）的HT检测工具，其特点在于针对不同HT检测场景设计了可调奖励函数。该工具能够探索现有检测策略，并几乎无需额外调整即可适应新检测场景。此外，我们提出一种用于公平比较不同HT检测方法的通用方法论。初步实验结果表明，在ISCAS-85基准测试中，我们的方法实现了平均84.2%的HT成功检测率。