Hardware trojan detection methods, based on machine learning (ML) techniques, mainly identify suspected circuits but lack the ability to explain how the decision was arrived at. An explainable methodology and architecture is introduced based on the existing hardware trojan detection features. Results are provided for explaining digital hardware trojans within a netlist using trust-hub trojan benchmarks.

翻译：基于机器学习（ML）技术的硬件木马检测方法主要识别可疑电路，但缺乏解释决策依据的能力。本文基于现有硬件木马检测特征，提出了一种可解释的方法与架构。通过使用Trust-Hub木马基准测试，提供了针对网表中数字硬件木马的解释结果。