论文标题
使用加固学习的硬件木马插入
Hardware Trojan Insertion Using Reinforcement Learning
论文作者
论文摘要
本文利用增强学习（RL）作为自动化硬件木马（HT）插入过程的一种手段，以消除限制强大HT检测方法发展的固有人类偏见。 RL代理探索设计空间，并找到最适合将插入的HT插入的电路位置。为此，数字电路转换为RL代理插入HTS的环境，从而最大程度地提高了累积奖励。我们的工具集可以将组合HTS插入ISCAS-85基准套件中，其HT尺寸和触发条件的变化。实验结果表明，该工具集可实现高输入覆盖率（在两个基准电路中100 \％），以证实其有效性。同样，插入的HTS显示出最小的足迹和罕见的激活概率。
This paper utilizes Reinforcement Learning (RL) as a means to automate the Hardware Trojan (HT) insertion process to eliminate the inherent human biases that limit the development of robust HT detection methods. An RL agent explores the design space and finds circuit locations that are best for keeping inserted HTs hidden. To achieve this, a digital circuit is converted to an environment in which an RL agent inserts HTs such that the cumulative reward is maximized. Our toolset can insert combinational HTs into the ISCAS-85 benchmark suite with variations in HT size and triggering conditions. Experimental results show that the toolset achieves high input coverage rates (100\% in two benchmark circuits) that confirms its effectiveness. Also, the inserted HTs have shown a minimal footprint and rare activation probability.
