Control-Theoretic Defense Strategies for Cyber-Physical Systems
网络物理系统的控制理论防御策略
基本信息
- 批准号:1405330
- 负责人:
- 金额:$ 38.64万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2014
- 资助国家:美国
- 起止时间:2014-09-01 至 2019-04-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
This project will design next-generation defense mechanisms to protect critical infrastructures, such as power grids, large industrial plants, and water distribution systems. These critical infrastructures are complex primarily due to the integration of cyber and physical components, the presence of high-order behaviors and functions, and an intricate and large interconnection pattern. Malicious attackers can exploit the complexity of the infrastructure, and compromise a system's functionality through cyber attacks (that is hacking into the computation and communication systems) and/or physical attacks (tampering with the actuators, sensors and the control system). This work will develop mathematical models of critical infrastructures and attacks, develop intelligent control-theoretic security mechanisms, and validate the findings on an industry-accredited simulation platform. This project will directly impact national security and economic competitiveness, and the results will be available and useful to utility companies. To accompany the scientific advances, the investigators will also engage in educational efforts to bring this research to the classroom at UCR, and will disseminate their results via scientific publications. The work will also create several opportunities for undergraduate and graduate students to engage in research at UCR, one of the nation's most ethnically diverse research-intensive institutions.This study encompasses theoretical, computational, and experimental research at UCR aimed at characterizing vulnerabilities of complex cyber-physical systems, with a focus on electric power networks, and control-theoretic defense mechanisms to ensure protection and graceful performance degradation against accidental faults and malicious attacks. This project proposes a transformative approach to cyber-physical security, which builds on a unified control-theoretic framework to model cyber-physical systems, attacks, and defense strategies. This project will undertake three main research initiatives ranging from fundamental scientific and engineering research to analysis using industry-accepted simulation tools: (1) modeling and analysis of cyber-physical attacks, and their impact on system stability and performance; (2) design of monitors to reveal and distinguish between accidental and malignant contingencies; and (3) synthesis of adaptive defense strategies for stochastic and highly dynamic cyber-physical systems. Results will first be characterized from a pure control-theoretic perspective with focus on stochastic, switching, and dynamic cyber-physical systems, so as to highlight fundamental research challenges, and then specialized for the case of smart grid, so as to clarify the implementation of monitors, attacks, and defense strategies. The findings and strategies will be validated for the case of power networks by using the RTDS simulation system, which is an industry-accredited tool for real-time tests of dynamic behavior, faults, attacks, monitoring systems, and defensive strategies.
该项目将设计下一代防御机制,以保护电网、大型工业工厂和供水系统等关键基础设施。这些关键基础设施之所以复杂,主要是因为网络和物理组件的集成、高阶行为和功能的存在以及复杂而庞大的互连模式。恶意攻击者可以利用基础设施的复杂性,通过网络攻击(即侵入计算和通信系统)和/或物理攻击(篡改执行器、传感器和控制系统)来危害系统的功能。这项工作将开发关键基础设施和攻击的数学模型,开发智能控制理论安全机制,并在行业认可的模拟平台上验证发现。该项目将直接影响国家安全和经济竞争力,其结果将对公用事业公司有用。为了配合科学进步,研究人员还将参与教育工作,将这项研究带入加州大学伯克利分校的课堂,并将通过科学出版物传播他们的结果。这项工作还将为本科生和研究生创造几个在UCR从事研究的机会,UCR是美国种族最多元化的研究机构之一。这项研究包括UCR的理论、计算和实验研究,旨在表征复杂网络物理系统的脆弱性,重点是电力网络,以及控制理论防御机制,以确保针对意外故障和恶意攻击的保护和优雅的性能降级。该项目提出了一种网络物理安全的变革性方法,它建立在一个统一的控制理论框架上,对网络物理系统、攻击和防御策略进行建模。该项目将开展三项主要研究活动,从基础科学和工程研究到使用业界公认的模拟工具进行分析:(1)网络物理攻击的建模和分析,及其对系统稳定性和性能的影响;(2)设计监视器以揭示和区分意外和恶性意外事件;以及(3)为随机和高度动态的网络物理系统综合自适应防御策略。结果将首先从纯控制论的角度进行表征,重点关注随机、切换和动态的网络物理系统,以突出基础研究挑战,然后专门针对智能电网的情况,以阐明监控、攻击和防御策略的实施。这些发现和策略将通过使用RTDS仿真系统在电力网络的情况下进行验证,RTDS仿真系统是一种行业认可的工具,用于实时测试动态行为、故障、攻击、监控系统和防御策略。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Fabio Pasqualetti其他文献
Continuous graph partitioning for camera network surveillance
- DOI:
10.1016/j.automatica.2014.11.017 - 发表时间:
2015-02-01 - 期刊:
- 影响因子:
- 作者:
Domenica Borra;Fabio Pasqualetti;Francesco Bullo - 通讯作者:
Francesco Bullo
On a security vs privacy trade-off in interconnected dynamical systems
- DOI:
10.1016/j.automatica.2020.109426 - 发表时间:
2021-03-01 - 期刊:
- 影响因子:
- 作者:
Vaibhav Katewa;Rajasekhar Anguluri;Fabio Pasqualetti - 通讯作者:
Fabio Pasqualetti
Noise in the reverse process improves the approximation capabilities of diffusion models
逆向过程中的噪声提高了扩散模型的逼近能力
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Karthik Elamvazhuthi;Samet Oymak;Fabio Pasqualetti - 通讯作者:
Fabio Pasqualetti
A network control theory pipeline for studying the dynamics of the structural connectome
用于研究结构连接组动态的网络控制理论管道
- DOI:
10.1038/s41596-024-01023-w - 发表时间:
2024-07-29 - 期刊:
- 影响因子:16.000
- 作者:
Linden Parkes;Jason Z. Kim;Jennifer Stiso;Julia K. Brynildsen;Matthew Cieslak;Sydney Covitz;Raquel E. Gur;Ruben C. Gur;Fabio Pasqualetti;Russell T. Shinohara;Dale Zhou;Theodore D. Satterthwaite;Dani S. Bassett - 通讯作者:
Dani S. Bassett
Denoising Diffusion-Based Control of Nonlinear Systems
非线性系统的基于去噪扩散的控制
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Karthik Elamvazhuthi;D. Gadginmath;Fabio Pasqualetti - 通讯作者:
Fabio Pasqualetti
Fabio Pasqualetti的其他文献
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{{ truncateString('Fabio Pasqualetti', 18)}}的其他基金
Collaborative Research: Analysis and Control of Nonlinear Oscillatory Networks for the Design of Novel Cortical Stimulation Strategies
合作研究:用于设计新型皮质刺激策略的非线性振荡网络的分析和控制
- 批准号:
2308639 - 财政年份:2023
- 资助金额:
$ 38.64万 - 项目类别:
Standard Grant
NCS-FO: Collaborative Research: Analysis, prediction, and control of synchronized neural activity
NCS-FO:协作研究:同步神经活动的分析、预测和控制
- 批准号:
1926829 - 财政年份:2019
- 资助金额:
$ 38.64万 - 项目类别:
Standard Grant
NCS-FO: Collaborative Research: A Mechanistic Model of Cognitive Control
NCS-FO:协作研究:认知控制的机制模型
- 批准号:
1631112 - 财政年份:2016
- 资助金额:
$ 38.64万 - 项目类别:
Standard Grant
CRCNS: Collaborative Research: Mapping and Control of Large-Scale Neural Dynamics
CRCNS:协作研究:大规模神经动力学的映射和控制
- 批准号:
1430279 - 财政年份:2014
- 资助金额:
$ 38.64万 - 项目类别:
Standard Grant
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