Collaborative Research: SaTC: CORE: Small: Enabling Programmable In-Network Security for an Attack-Resilient Smart Grid

协作研究:SaTC:核心:小型:为抵御攻击的智能电网实现可编程网内安全

基本信息

  • 批准号:
    2247722
  • 负责人:
  • 金额:
    $ 28万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-06-01 至 2026-05-31
  • 项目状态:
    未结题

项目摘要

Modern energy systems are increasingly adopting Internet technology to boost efficiency, which opens new security frontiers. On the one hand, the underlying network infrastructure used by existing power grids significantly limits the performance of current security measures. On the other hand, Internet companies are undergoing a foundational change in their network infrastructure, mainly driven by deploying advanced programmable network architectures. This project aims to create and quantify programmable in-network security measures for an attack-resilient power grid. Achieving this objective involves integrating the advancement of data-plane programmability into energy system cyber infrastructure, and based on this, further exploring numerous applications across attack detection, mitigation, and prevention to secure today’s energy systems. The outcomes of the project will have significant impacts on industrial practice through the suitable adoption of the proposed programmable cyber-infrastructure to realize a defense-in-depth approach for future energy systems. The work will also train students and workforce with cutting-edge and cross-disciplinary knowledge in power systems, networking, and cyber-security. The project aims to build the next-generation programmable cyber-infrastructure for energy system protection with three research thrusts: (i) an in-network cyber-attack detection system with high-speed and customizable packet inspection; (ii) a self-healing system that automatically restores network connectivity and recovers corrupted measurements by considering characteristics from both communication networks and physical devices; and (iii) an in-network traffic scheduler that disrupts attack reconnaissance while maintaining quality-of-service requirements. The impact of the proposed research will lay a scientific foundation for system design through the development of models, algorithms, and tools that incorporate both cyber and physical system properties.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.
现代能源系统越来越多地采用互联网技术来提高效率,这开辟了新的安全前沿。 一方面,现有电网使用的底层网络基础设施极大地限制了当前安全措施的性能。其网络基础设施发生了根本性变化,主要是通过部署先进的可编程网络架构来推动的,该项目旨在为抵御攻击的电网创建和量化可编程的网络内安全措施,实现这一目标涉及整合数据平面可编程性的进步。深入能源系统网络基础设施,并在此基础上进一步探索攻击检测、缓解和预防方面的众多应用,以保护当今的能源系统。该项目的成果将通过适当采用拟议的可编程网络基础设施对工业实践产生重大影响。 -实现未来能源系统纵深防御方法的基础设施。该项目还将培训学生和劳动力,使其具备电力系统、网络和网络安全方面的前沿和跨学科知识。下一代可编程网络基础设施能源系统保护具有三个研究重点:(i)具有高速和可定制数据包检查的网络内网络攻击检测系统;(ii)自我修复系统,可通过考虑以下特征来自动恢复网络连接并恢复损坏的测量结果通信网络和物理设备;以及(iii)网络内流量调度程序,该调度程序可以在保持服务质量要求的同时中断攻击侦察。所提出的研究的影响将为通过模型开发的系统设计奠定科学基础。算法和工具该奖项反映了网络和物理系统的属性。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。

项目成果

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Hui Lin其他文献

Counterfactual Debating with Preset Stances for Hallucination Elimination of LLMs
预设立场的反事实辩论消除法学硕士的幻觉
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Yi Fang;Moxin Li;Wenjie Wang;Hui Lin;Fuli Feng
  • 通讯作者:
    Fuli Feng
Perspective clinical study on effect of 5-aminolevulinic acid photodynamic therapy (ALA-PDT) in treating condylomata acuminata in pregnancy.
5-氨基乙酰丙酸光动力疗法(ALA-PDT)治疗妊娠期尖锐湿疣效果的前瞻性临床研究
A Tag Identification Approach Based On Fragile Watermark
一种基于脆弱水印的标签识别方法
  • DOI:
  • 发表时间:
    2014
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Jianbiao Lin;Ke Ji;Hui Lin;Enyan Wu;Xin Gao
  • 通讯作者:
    Xin Gao
Space-time disease mapping by combining Bayesian maximum entropy and Kalman filter: the BME-Kalman approach
结合贝叶斯最大熵和卡尔曼滤波器的时空疾病绘图:BME-卡尔曼方法
Feasibility of ASL spinal bone marrow perfusion imaging with optimized inversion time
优化反转时间的 ASL 脊髓骨髓灌注成像的可行性
  • DOI:
    10.1002/jmri.24891
  • 发表时间:
    2015
  • 期刊:
  • 影响因子:
    4.4
  • 作者:
    Dong Xing;Y. Zha;L. Yan;Kejun Wang;Wei Gong;Hui Lin
  • 通讯作者:
    Hui Lin

Hui Lin的其他文献

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{{ truncateString('Hui Lin', 18)}}的其他基金

CAREER: PARP: Mislead Physical-Disruption Attacks by Preemptive Anti-Reconnaissance for Power Grids Cyber-Physical Infrastructures
职业: PARP:通过对电网网络物理基础设施的先发制人的反侦察来误导物理破坏攻击
  • 批准号:
    2144513
  • 财政年份:
    2022
  • 资助金额:
    $ 28万
  • 项目类别:
    Continuing Grant
CRII: SaTC: Preempting Physical Damage from Control-Related Attacks on Smart Grids' Cyber-Physical Infrastructure
CRII:SaTC:预防对智能电网网络物理基础设施的控制相关攻击造成的物理损害
  • 批准号:
    2041643
  • 财政年份:
    2020
  • 资助金额:
    $ 28万
  • 项目类别:
    Standard Grant
CRII: SaTC: Preempting Physical Damage from Control-Related Attacks on Smart Grids' Cyber-Physical Infrastructure
CRII:SaTC:预防对智能电网网络物理基础设施的控制相关攻击造成的物理损害
  • 批准号:
    1850377
  • 财政年份:
    2019
  • 资助金额:
    $ 28万
  • 项目类别:
    Standard Grant

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职场网络闲逛行为的作用结果及其反馈效应——基于行为者和观察者视角的整合研究
  • 批准号:
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相似海外基金

Collaborative Research: SaTC: CORE: Medium: Differentially Private SQL with flexible privacy modeling, machine-checked system design, and accuracy optimization
协作研究:SaTC:核心:中:具有灵活隐私建模、机器检查系统设计和准确性优化的差异化私有 SQL
  • 批准号:
    2317232
  • 财政年份:
    2024
  • 资助金额:
    $ 28万
  • 项目类别:
    Continuing Grant
Collaborative Research: SaTC: CORE: Medium: Using Intelligent Conversational Agents to Empower Adolescents to be Resilient Against Cybergrooming
合作研究:SaTC:核心:中:使用智能会话代理使青少年能够抵御网络诱骗
  • 批准号:
    2330940
  • 财政年份:
    2024
  • 资助金额:
    $ 28万
  • 项目类别:
    Continuing Grant
Collaborative Research: NSF-BSF: SaTC: CORE: Small: Detecting malware with machine learning models efficiently and reliably
协作研究:NSF-BSF:SaTC:核心:小型:利用机器学习模型高效可靠地检测恶意软件
  • 批准号:
    2338301
  • 财政年份:
    2024
  • 资助金额:
    $ 28万
  • 项目类别:
    Continuing Grant
Collaborative Research: SaTC: CORE: Medium: Differentially Private SQL with flexible privacy modeling, machine-checked system design, and accuracy optimization
协作研究:SaTC:核心:中:具有灵活隐私建模、机器检查系统设计和准确性优化的差异化私有 SQL
  • 批准号:
    2317233
  • 财政年份:
    2024
  • 资助金额:
    $ 28万
  • 项目类别:
    Continuing Grant
Collaborative Research: NSF-BSF: SaTC: CORE: Small: Detecting malware with machine learning models efficiently and reliably
协作研究:NSF-BSF:SaTC:核心:小型:利用机器学习模型高效可靠地检测恶意软件
  • 批准号:
    2338302
  • 财政年份:
    2024
  • 资助金额:
    $ 28万
  • 项目类别:
    Continuing Grant
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