arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1602.06604eess.SYcs.SIcs.SYphysics.data-anphysics.soc-phstat.AP
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从物理相关性检测网络物理故障和入侵

Detection of Cyber-Physical Faults and Intrusions from Physical Correlations

Los Alamos National Laboratory(洛斯阿拉莫斯国家实验室);University of Vermont(佛蒙特大学)

Andrey Y. Lokhov, Nathan Lemons, Thomas C. McAndrew, Aric Hagberg, Scott Backhaus

AI总结:

研究通过物理信号相关性高效识别故障,无需先验知识,验证于建筑自动化系统数据。

摘要:

网络物理系统是关键基础设施,对资源可靠交付和自动控制架构稳定运行至关重要。这些系统由相互依赖的物理、控制和通信网络组成,由不同的数学模型描述,带来超出单个网络建模和分析的科学挑战。网络物理防御的关键挑战是快速在线检测和定位故障和入侵,而无需事先了解故障类型。我们描述了一套技术,通过物理信号中的相关性高效识别故障,仅假设少量可用系统信息。我们的检测方法性能在大型建筑自动化系统收集的数据上进行了说明。

英文摘要:

Cyber-physical systems are critical infrastructures that are crucial both to the reliable delivery of resources such as energy, and to the stable functioning of automatic and control architectures. These systems are composed of interdependent physical, control and communications networks described by disparate mathematical models creating scientific challenges that go well beyond the modeling and analysis of the individual networks. A key challenge in cyber-physical defense is a fast online detection and localization of faults and intrusions without prior knowledge of the failure type. We describe a set of techniques for the efficient identification of faults from correlations in physical signals, assuming only a minimal amount of available system information. The performance of our detection method is illustrated on data collected from a large building automation system.

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。

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