课题基金 / 基金详情

CPS: Small: Data-driven Real-time Data Authentication in Wide-Area Energy Infrastructure Sensor Networks

CPS: Small: Data-driven Real-time Data Authentication in Wide-Area Energy Infrastructure Sensor Networks
CPS:小型:广域能源基础设施传感器网络中数据驱动的实时数据身份验证
批准号:
1931975
负责人:
Yilu Liu
金额:
$40.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-09-15 至 2024-08-31
关键词:

项目摘要

项目成果

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中文摘要
翻译
电网是一个大规模动态的网络物理系统,对现代社会至关重要。为了确保其可靠运行,广域监测和控制系统必须实时了解系统动态,以便能够实时采取适当的控制措施,以响应电力系统扰动,避免停电。与许多其他CPSS一样,由于监控协议缺乏安全属性,电网很容易受到数据欺骗攻击。考虑到快速变化的系统动态,许多电力系统设备无法忍受传统的密码技术,因为计算负担和时延是无法忍受的。该项目提出开发基于空间和时间签名的方法和工具,高效地认证电网广域测量,并有效地防止恶意网络攻击造成灾难性损失。通过提供检测网络攻击的数据认证工具,该项目将提供一条利用CPS本身的测量数据来保护广域高动态闭环实时CPS的安全的新途径。该项目将对电网安全产生立竿见影的积极影响,也将对其他紧密耦合、物理分布的CPSS有利。该项目还将为K-12和STEM学生提供许多教育资源和机会,以扩大他们对工程的参与。该项目的主要目标是解决电力系统的网络安全问题,并帮助保护电力系统免受现有技术难以检测的数据欺骗攻击。项目组发现,电网广域监测系统(WAMS)的测量数据具有空间和时间特征,这是由于每个本地电网的连续和随机状态变化的自然响应造成的。这些签名几乎不可能被伪造,这使它们成为数据认证的完美选择。该项目将首次在高数据速率(高达1,500个样本/秒)测量中使用签名,以开发数据驱动的网络物理安全工具。实现项目目标的主要步骤包括:(1)研究有效地提取WAMS测量中嵌入的空间和时间特征的方法;(2)开发用于实时认证大量WAMS测量数据的方法和工具;(3)在FNET/GridEye这一实际的网络物理系统上在线实施和演示网络攻击检测技术。有了安全的网络系统提供的可靠实时信息,电力系统运营商将能够评估系统风险或迅速采取行动,控制和缓解风险,防止停电。安全的网络系统还将确保物理电力系统处于自动控制器确定的适当控制之下,以防止系统进入危险状态。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
The electric power grid is a large-scale dynamic cyber-physical systems (CPS) and it is critical to the modern society. To ensure its reliable operation, wide-area monitoring and control systems are essential to provide real-time understanding of the system dynamics such that proper control actions can be taken in real time to respond to power system disturbances and avoid blackouts. Like many other CPSs, power grids are vulnerable to data spoofing attacks due to the lack of security properties in monitoring and control protocols. Conventional cryptography techniques are not usable because the computation burden and time delay are intolerable to numerous power system devices considering the fast-changing system dynamics. This project proposes to develop spatial and temporal signature-based methods and tools that efficiently authenticate power grid wide-area measurements and effectively prevent malicious cyber-attacks from causing catastrophic losses. By delivering data authentication tools to detect cyber-attacks, this project will provide a new path that leverages measurement data from CPS itself to protect the security of wide-area highly-dynamic closed-loop real-time CPS. This project will have immediate positive impacts on electric power grid security and will also be beneficial to other tightly-coupled, physically-distributed CPSs. The project will also provide many educational resources and opportunities for K-12 and STEM students to broaden their participation in engineering.The project's primary goal is to address power system cyber security issues and help protect the power system from data spoofing attacks that are hard to detect by existing technologies. The project team discovered that power grid wide-area monitoring system (WAMS) measurement data have spatial and temporal signatures caused by natural responses of each local grid's continuous and random condition changes. These signatures are almost impossible to counterfeit, making them perfect for data authentication. This project will be the first time to use signatures in high data rate (up to 1,500 sample per second) measurements to develop data-driven cyber-physical security tools. Major steps to achieve the project goal include: (1) investigate methods to extract spatial and temporal signatures embedded in WAMS measurements efficiently; (2) develop methods and tools for authenticating a large volume of WAMS measurement data in real time; and (3) online implement and demonstrate cyber-attack detection technologies on FNET/GridEye, which is an actual cyber-physical system. With credible real-time information provided by the secure cyber system, power system operators will be able to assess system risks or take prompt actions to control and mitigate risks and prevent blackouts. The secured cyber system will also ensure the physical power system is under appropriate controls determined by automatic controllers to prevent systems entering dangerous status.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
期刊论文(10)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1016/j.epsr.2021.107207
发表时间: 2021-07
期刊: Electric Power Systems Research
影响因子: 3.9
作者: [Shengyuan Liu;Shutang You;Chujie Zeng;H. Yin;Zhenzhi Lin;Yuqing Dong;W. Qiu;Wenxuan Yao;Yilu Liu]
通讯作者: Shengyuan Liu;Shutang You;Chujie Zeng;H. Yin;Zhenzhi Lin;Yuqing Dong;W. Qiu;Wenxuan Yao;Yilu Liu
DOI: 10.1109/pesgm46819.2021.9638108
发表时间: 2020-10
期刊: 2021 IEEE Power & Energy Society General Meeting (PESGM)
影响因子: --
作者: [Shutang You;Hongyu Li;Shengyuan Liu;Kaiqi Sun;Weikang Wang;W. Qiu;Yilu Liu]
通讯作者: Shutang You;Hongyu Li;Shengyuan Liu;Kaiqi Sun;Weikang Wang;W. Qiu;Yilu Liu
DOI: 10.1109/pesgm48719.2022.9916706
发表时间: 2020-10
期刊: 2022 IEEE Power & Energy Society General Meeting (PESGM)
影响因子: --
作者: [Shutang You;Yilu Liu]
通讯作者: Shutang You;Yilu Liu
DOI: 10.1109/tpwrd.2022.3173974
发表时间: 2022-12
期刊: IEEE Transactions on Power Delivery
影响因子: 4.4
作者: [Shengyuan Liu;Kaiqi Sun;Chujie Zeng;Shutang You;Hongyu Li;Wenpeng Yu;Xianda Deng;Zhenzhi Lin;Yilu Liu]
通讯作者: Shengyuan Liu;Kaiqi Sun;Chujie Zeng;Shutang You;Hongyu Li;Wenpeng Yu;Xianda Deng;Zhenzhi Lin;Yilu Liu
共 10 条
    AI-Assisted Algorithms for Automatic AC Power Flow Model Creation based on DC Dispatch
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      2243204
    • 项目类别:
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    • 资助金额:
      $35.0万
    • 财政年份:
      2023
    • 负责人:
      Yilu Liu
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    PFI-RP: Increasing the stability of large-scale electric power systems through an adaptive measurement-driven controller prototype.
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      1941101
    • 项目类别:
      Standard Grant
    • 资助金额:
      $54.0万
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      2020
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      Yilu Liu
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    MRI: Development of Pulsar-based Power Grid Timing Instrumentation and Technology
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      1920025
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      Standard Grant
    • 资助金额:
      $100.0万
    • 财政年份:
      2019
    • 负责人:
      Yilu Liu
    • 依托单位:
    EAGER: Real-Time: Intelligent Mitigation of Low-Frequency Oscillations in Smart Grid Using Real-time Learning
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      1839684
    • 项目类别:
      Standard Grant
    • 资助金额:
      $27.58万
    • 财政年份:
      2018
    • 负责人:
      Yilu Liu
    • 依托单位:
    国内基金
    海外基金
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      省市级项目
    • 资助金额:
      --
    • 批准年份:
      2024
    • 负责人:
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    tRNA-derived small RNA上调YBX1/CCL5通路参与硼替佐米诱导慢性疼痛的机制研究
    • 批准号:
    • 项目类别:
      省市级项目
    • 资助金额:
      10.0万元
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      2022
    • 负责人:
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    • 批准号:
      31972324
    • 项目类别:
      面上项目
    • 资助金额:
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    • 批准年份:
      2019
    • 负责人:
      高学文
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