基于代码突变的信息物理系统的学习和不变量
Towards Learning and Verifying Invariants of Cyber-Physical Systems by Code Mutation
摘要：
信息物理系统（CPS），它集成了算法控制与物理过程，通常由物理分布的组件通过网络进行通信。这种CPS中的故障或受损组件可能导致代价高昂的后果，特别是在公共基础设施的背景下。在这篇简短的论文中，我们认为构建CPS所表现出的物理行为的不变量（或模型）的重要性，其动机是它们的应用程序的控制，监控和认证的组件。为了实现这一目标，尽管CPS具有固有的复杂性，我们提出了一种学习不变量的新技术，该技术将机器学习与突变测试的想法相结合。我们提出了一个水处理系统的初步研究，表明这种方法的有效性，提出了建立信心的不变量的正确性的策略，然后总结了一些研究问题和我们正在采取的步骤来调查他们。
英文摘要：
Cyber-physical systems (CPS), which integrate algorithmic control with physical processes, often consist of physically distributed components communicating over a network. A malfunctioning or compromised component in such a CPS can lead to costly consequences, especially in the context of public infrastructure. In this short paper, we argue for the importance of constructing invariants (or models) of the physical behaviour exhibited by CPS, motivated by their applications to the control, monitoring, and attestation of components. To achieve this despite the inherent complexity of CPS, we propose a new technique for learning invariants that combines machine learning with ideas from mutation testing. We present a preliminary study on a water treatment system that suggests the efficacy of this approach, propose strategies for establishing confidence in the correctness of invariants, then summarise some research questions and the steps we are taking to investigate them.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
