Cyber-Physical Systems (CPS) have been widely deployed in safety-critical domains such as transportation, power and energy. Recently, there comes an increasing demand in employing deep neural networks (DNNs) in CPS for more intelligent control and decision making in sophisticated industrial safety-critical conditions, giving birth to the class of DNN controllers. However, due to the inherent uncertainty and opaqueness of DNNs, concerns about the safety of DNN-enabled CPS are also surging. In this work, we propose an automated framework named AutoRepair that, given a safety requirement, identifies unsafe control behavior in a DNN controller and repairs them through an optimization-based method. Having an unsafe signal of system execution, AutoRepair iteratively explores the control decision space and searches for the optimal corrections for the DNN controller in order to satisfy the safety requirements. We conduct a comprehensive evaluation of AutoRepair on 6 instances of industry-level DNN-enabled CPS from different safety-critical domains. Evaluation results show that AutoRepair successfully repairs critical safety issues in the DNN controllers, and significantly improves the reliability of CPS.

翻译：信息物理系统已在交通、电力能源等安全关键领域得到广泛应用。近年来，在复杂工业安全关键场景中，为提升智能控制与决策能力而将深度神经网络应用于信息物理系统的需求日益增长，由此催生了深度神经网络控制器。然而，由于深度神经网络固有的不确定性与不透明性，关于深度神经网络赋能信息物理系统安全性的担忧也日益凸显。本文提出一种名为AutoRepair的自动化框架，该框架能够依据安全需求识别深度神经网络控制器中的不安全控制行为，并通过基于优化的方法进行修复。当检测到系统执行的不安全信号时，AutoRepair通过迭代探索控制决策空间，为深度神经网络控制器搜索最优修正方案以满足安全需求。我们在来自不同安全关键领域的6个工业级深度神经网络赋能信息物理系统实例上开展了全面评估。评估结果表明，AutoRepair成功修复了深度神经网络控制器中的关键安全问题，显著提升了信息物理系统的可靠性。