论异常作为物理故障或执行器网络攻击的可区分性
On Distinguishability of Anomalies as Physical Faults or Actuation Cyberattacks
AI总结：
本文提出一种基于滑模未知输入观测器的数学框架，用于区分基础设施中的物理故障与执行器网络攻击，并通过仿真验证其有效性。
摘要：
自动化程度的提高促使人们将基础设施与广泛的连接性相集成，以提高效率、可持续性、自主性和安全性。然而，这种对连接性的依赖以及系统复杂性的不可避免性，增加了系统对物理故障或退化以及外部网络威胁的脆弱性。然而，应对故障和网络攻击的策略将大不相同，因此不仅需要检测这些系统中存在的异常，而且还需要识别其性质，这一点至关重要。在这项工作中，我们提出了一种数学框架，利用基于滑模的未知输入观测器来区分物理故障和网络攻击。最后，我们通过仿真案例研究，使用所提出的可区分性度量和准则来区分物理故障和网络攻击。仿真结果表明，所提出的框架成功地区分了故障和网络攻击。
英文摘要：
Increased automation has created an impetus to integrate infrastructure with wide-spread connectivity in order to improve efficiency, sustainability, autonomy, and security. Nonetheless, this reliance on connectivity and the inevitability of complexity in this system increases the vulnerabilities to physical faults or degradation and external cyber-threats. However, strategies to counteract faults and cyberattacks would be widely different and thus it is vital to not only detect but also to identify the nature of the anomaly that is present in these systems. In this work, we propose a mathematical framework to distinguish between physical faults and cyberattack using a sliding mode based unknown input observer. Finally, we present simulation case studies to distinguish between physical faults and cyberattacks using the proposed Distinguishability metric and criterion. The simulation results show that the proposed framework successfully distinguishes between faults and cyberattacks.
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。
