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Control-Theoretic Defense Strategies for Cyber-Physical Systems

Control-Theoretic Defense Strategies for Cyber-Physical Systems
网络物理系统的控制理论防御策略
批准号:
1405330
负责人:
Fabio Pasqualetti
金额:
$38.64万
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
项目状态:
已结题
起止时间:
2014-09-01 至 2019-04-30

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中文摘要
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英文摘要
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.
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Collaborative Research: Analysis and Control of Nonlinear Oscillatory Networks for the Design of Novel Cortical Stimulation Strategies
  • 批准号:
    2308639
  • 项目类别:
    Standard Grant
  • 资助金额:
    $30.0万
  • 财政年份:
    2023
  • 负责人:
    Fabio Pasqualetti
  • 依托单位:
NCS-FO: Collaborative Research: Analysis, prediction, and control of synchronized neural activity
  • 批准号:
    1926829
  • 项目类别:
    Standard Grant
  • 资助金额:
    $49.95万
  • 财政年份:
    2019
  • 负责人:
    Fabio Pasqualetti
  • 依托单位:
NCS-FO: Collaborative Research: A Mechanistic Model of Cognitive Control
  • 批准号:
    1631112
  • 项目类别:
    Standard Grant
  • 资助金额:
    $21.01万
  • 财政年份:
    2016
  • 负责人:
    Fabio Pasqualetti
  • 依托单位:
CRCNS: Collaborative Research: Mapping and Control of Large-Scale Neural Dynamics
  • 批准号:
    1430279
  • 项目类别:
    Standard Grant
  • 资助金额:
    $34.39万
  • 财政年份:
    2014
  • 负责人:
    Fabio Pasqualetti
  • 依托单位:
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