NeTS: Small: Collaborative Research: Measurement and Modeling of Industrial Control Networks

NeTS：小型：协作研究：工业控制网络的测量和建模

• 批准号: 1718848
• 负责人: Alvaro Cardenas
• 金额: $ 24.93万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-10-01 至 2019-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1718848&HistoricalAwards=false
• 关键词: NeTS; Small; Collaborative; Research; Measurement

Operators of industrial control systems have limited software tools for obtaining situational awareness of their networks and processes. This project seeks to characterize network traffic for a rich variety of industrial control networks. Because the research community lacks access to (mostly proprietary) data sets from operating control systems, our ability to identify anomalous behavior and protect weaknesses is a concern for critical U.S. infrastructure. The research team has obtained a collection of network data sets that will allow them to study the performance (and security) properties of various types of industrial control systems, including wastewater treatment, power, and solar energy. By examining the similarities and differences of these data sets, this analysis will yield models of critical infrastructure control system traffic that will aid in anomaly detection and further our understanding of these networks in general.The project will characterize network flows, and perform deep-packet inspection in a protocol- and process-aware fashion. The project will develop open-source tools implementing anomaly detection algorithms to be used by researchers and the process control industry. Since the anomaly detection tools are passive, they can ultimately be deployed in the operational environments of industrial partners to help them improve the visibility and understanding of their control networks. This work will be performed in collaboration with researchers in Israel under the US-Israel Binational Science Foundation joint funding program.

工业控制系统的运营商具有有限的软件工具来获得其网络和流程的情境意识。该项目旨在表征各种工业控制网络的网络流量。 由于研究界无法从操作控制系统获得（主要是专有）数据集，因此我们识别异常行为和保护弱点的能力是关键的美国基础设施的关注点。 研究团队获得了网络数据集的集合，使他们能够研究各种类型的工业控制系统的性能（和安全性）特性，包括废水处理，功率和太阳能。通过检查这些数据集的相似性和差异，该分析将产生关键的基础设施控制系统流量的模型，这些模型将有助于进行异常检测并进一步了解这些网络。该项目将表征网络流，并以协议和过程意识的方式进行深度模拟检查。 该项目将开发开源工具，以实现研究人员和过程控制行业使用的异常检测算法。由于异常检测工具是被动的，因此最终可以在工业伙伴的运营环境中部署它们，以帮助他们提高对控制网络的可见性和理解。这项工作将与以色列双印度科学基金会联合资助计划与以色列的研究人员合作进行。