Insertion of hardware Trojans (HTs) in integrated circuits is a pernicious threat. Since HTs are activated under rare trigger conditions, detecting them using random logic simulations is infeasible. In this work, we design a reinforcement learning (RL) agent that circumvents the exponential search space and returns a minimal set of patterns that is most likely to detect HTs. Experimental results on a variety of benchmarks demonstrate the efficacy and scalability of our RL agent, which obtains a significant reduction ($169\times$) in the number of test patterns required while maintaining or improving coverage ($95.75\%$) compared to the state-of-the-art techniques.

翻译：在集成电路中插入硬硬特洛伊(HT)是一种有害威胁;由于HT是在罕见的触发条件下启动的,因此使用随机逻辑模拟来探测它们是不可行的;在这项工作中,我们设计了一个强化学习(RL)剂,绕过指数搜索空间,返回最有可能探测HT的最低限度模式。 各种基准的实验结果显示,与最新技术相比,我们RL剂的功效和可扩缩性显著减少(169美元),同时保持或改进覆盖范围(95.75美元)。