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, an RL agent explores the circuits and finds locations best for keeping inserted HTs hidden. On the defense side, we introduce a multi-criteria RL-based HT detector that generates test vectors to discover the existence of HTs. Using the proposed framework, one can explore the HT insertion and detection design spaces to break the limitations of human mindset and benchmark issues, ultimately leading toward the next generation of innovative detectors. We demonstrate the efficacy of our framework on ISCAS-85 benchmarks, provide the attack and detection success rates, and define a methodology for comparing our techniques.

翻译：当前的硬件木马（HT）检测技术大多基于有限的HT基准测试集开发。现有HT基准电路存在多个缺陷，即：i）在创建时严重受到设计者思维模式的影响，ii）通过单一维度（主要为网线的信号活动）生成。我们首次提出自动化强化学习（RL）HT插入与检测框架以解决这些缺陷。在HT插入阶段，RL代理探索电路并寻找最适合隐藏插入HT的位置。在防御方面，我们引入基于多准则RL的HT检测器，通过生成测试向量来发现HT的存在。利用所提框架，可探索HT插入与检测的设计空间，突破人类思维局限与基准测试问题，最终推动下一代创新检测器的发展。我们在ISCAS-85基准测试上验证了框架的有效性，提供了攻击与检测成功率，并定义了用于对比我们技术的评估方法论。