一句话：提出基于可解释 AI 的灰盒模糊测试，通过识别关键执行器缩减 ICS 状态空间，较黑盒方法加速 64 倍。
论文针对工业控制系统（ICS）分析中白盒方法依赖复杂领域知识、黑盒模糊测试因状态爆炸难以扩展到大量传感器与执行器的问题，提出基于可解释 AI（XAI）的灰盒模糊测试。该方法利用机器学习和可解释 AI 对 ICS 建模，自动识别对系统安全性关键的一小部分执行器，从而在不依赖领域专家的情况下显著压缩待探索状态空间。评估表明，该方法能够准确解释 ICS 模型，并与传统黑盒模糊测试相比将测试速度提升 64 倍，提升了大规模 ICS 分析的可扩展性。
Conventional approaches to analyzing industrial control systems have relied on either white-box analysis or black-box fuzzing. However, white-box methods rely on sophisticated domain expertise, while black-box methods suffers from state explosion and thus scales poorly when analyzing real ICS involving a large number of sensors and actuators. To address these limitations, we propose XAI-based gray-box fuzzing, a novel approach that leverages explainable AI and machine learning modeling of ICS to accurately identify a small set of actuators critical to ICS safety, which result in significant reduction of state space without relying on domain expertise. Experiment results show that our method accurately explains the ICS model and significantly speeds-up fuzzing by 64x when compared to conventional black-box methods.