CPS: TTP Option: Synergy: A Verifiable Framework for Cyber- Physical Attacks and Countermeasures in a Resilient Electric Power Grid
CPS: TTP Option: Synergy: A Verifiable Framework for Cyber- Physical Attacks and Countermeasures in a Resilient Electric Power Grid
批准号:
1449080
负责人:
Lalitha Sankar
金额:
$140.0万
依托单位:
依托单位国家:
美国
项目类别:
Cooperative Agreement
财政年份:
2015
资助国家:
美国
项目状态:
已结题
起止时间:
2015-03-01 至 2020-02-29
中文摘要
电网是一个网络物理系统(CPS),面临着网络攻击造成灾难性破坏的惊人高风险。然而,建模网络攻击,评估后果,并制定适当的对策需要一个详细的,现实的,易于处理的电力CPS运营模型。主要的障碍是缺乏对复杂的传统专有系统模型的访问,而这些系统是电网几十年来所依赖的。该项目旨在克服这些挑战,开发一个攻击验证(可验证)软件框架,该框架将捕获足够详细的电力系统操作。网络威胁将使用该框架通过合理的理论方法和开源商业模拟引擎的组合进行验证,该引擎可通过独特的过渡到实践(TTP)选项访问。这项研究的重点是四个基本的和相关的推力:(一)确定类的网络攻击与可量化的物理后果,并制定基于检测的对策;(二)确定对分布式电网运营的通信攻击,并制定信息共享对策;(iii)开发一个可验证的软件框架,模拟电网与(i)和(ii)推力同步的时空运行验证攻击模型,评估对策,并开发新的弹性协议;以及(iv)TTP选项,与来自IncSys和PowerData的行业领先专家合作,开发商业级开源电力仿真软件包,以集成和测试推力(i)至(iii)的攻击和对策,并为北美电力可靠性理事会(NERC)开发劳动力培训课程认证这项研究还包括通过亚利桑那科学实验室计划与K-12学生的接触。
英文摘要
The electric power grid, a cyber-physical system (CPS), faces an alarmingly high risk of catastrophic damage from cyber-attacks. However, modeling cyber-attacks, evaluating consequences, and developing appropriate countermeasures require a detailed, realistic, and tractable model of electric power CPS operations. The primary barrier is the lack of access to models for the complex legacy proprietary systems upon which the electric power grid has relied for decades. This project aims to overcome these challenges with the development of an attack-verifying (verifiable) software framework that will capture the electric power system operations in adequate detail. Cyber threats will be verified using this framework through a combination of sound theoretical methods and an open-source commercial simulation engine accessible via a unique transition to practice (TTP) option. This research focuses on four fundamental and related thrusts: (i) identifying classes of cyber-attacks with quantifiable physical consequences and developing detection-based countermeasures; (ii) identifying communication attacks on distributed grid operations and developing information-sharing countermeasures; (iii) developing a verifiable software framework that models the spatio-temporal operations of the electric grid in tandem with thrusts (i) and (ii) to verify attack models, evaluate countermeasures, and develop new resiliency protocols; and (iv) a TTP option, in collaboration with industry-leading experts from IncSys and PowerData, to develop commercial grade open source power simulation software packages to integrate and test the attacks and countermeasures of Thrusts (i) through (iii) as well as develop workforce training curriculum for North American Electric Reliability Council (NERC) certification. This research also includes engagement with K-12 students via the Arizona Science Laboratory program.
期刊论文(3)
专著(0)
科研奖励(0)
会议论文
DOI:
10.1049/iet-stg.2020.0030
发表时间:
2020-03
期刊:
ArXiv
影响因子:
--
作者:
[Zhigang Chu;O. Kosut;L. Sankar]
通讯作者:
Zhigang Chu;O. Kosut;L. Sankar
DOI:
10.1109/smartgridcomm.2019.8909739
发表时间:
2019-05
期刊:
2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
影响因子:
--
作者:
[Zhigang Chu;Andrea Pinceti;R. Biswas;O. Kosut;A. Pal;L. Sankar]
通讯作者:
Zhigang Chu;Andrea Pinceti;R. Biswas;O. Kosut;A. Pal;L. Sankar
Data-Driven Generation of Synthetic Load Datasets Preserving Spatio-Temporal Features
数据驱动的合成负载数据集生成保留时空特征
DOI:
10.1109/pesgm40551.2019.8973532
发表时间:
2019
期刊:
2019 IEEE Power & Energy Society General Meeting (PESGM
影响因子:
--
作者:
[Pinceti, Andrea, Kosut, Oliver, Sankar, Lalitha]
通讯作者:
Sankar, Lalitha
Exploiting Physical and Dynamical Structures for Real-time Inference in Electric Power Systems
-
批准号:2246658
-
项目类别:Standard Grant
-
资助金额:$36.0万
-
财政年份:2023
-
负责人:Lalitha Sankar
-
依托单位:
Collaborative Research: SCH: Fair Federated Representation Learning for Breast Cancer Risk Scoring
-
批准号:2205080
-
项目类别:Standard Grant
-
资助金额:$30.0万
-
财政年份:2022
-
负责人:Lalitha Sankar
-
依托单位:
Unifying Information- and Optimization-Theoretic Approaches for Modeling and Training Generative Adversarial Networks
-
批准号:2134256
-
项目类别:Continuing Grant
-
资助金额:$110.0万
-
财政年份:2021
-
负责人:Lalitha Sankar
-
依托单位:
RAPID: SaTC: FACT: Federated Analytics based Contact Tracing for COVID-19
-
批准号:2031799
-
项目类别:Standard Grant
-
资助金额:$20.0万
-
财政年份:2020
-
负责人:Lalitha Sankar
-
依托单位:
CIF: Small: Alpha Loss: A New Framework for Understanding and Trading Off Computation, Accuracy, and Robustness in Machine Learning
-
批准号:2007688
-
项目类别:Standard Grant
-
资助金额:$50.8万
-
财政年份:2020
-
负责人:Lalitha Sankar
-
依托单位:
Student Travel Support for the 2020 IEEE SGComm Conference. To be Held November, 11-13, 2020 at Arizona State University.
-
批准号:2024805
-
项目类别:Standard Grant
-
资助金额:$0.88万
-
财政年份:2020
-
负责人:Lalitha Sankar
-
依托单位:
CIF: Medium: Collaborative Research: Information-theoretic Guarantees on Privacy in the Age of Learning
-
批准号:1901243
-
项目类别:Continuing Grant
-
资助金额:$81.7万
-
财政年份:2019
-
负责人:Lalitha Sankar
-
依托单位:
Collaborative Research: High-Dimensional Spatio-Temporal Data Science for a Resilient Power Grid: Towards Real-Time Integration of Synchrophasor Data
-
批准号:1934766
-
项目类别:Continuing Grant
-
资助金额:$131.4万
-
财政年份:2019
-
负责人:Lalitha Sankar
-
依托单位:
CIF: Small: Collaborative Research: Generative Adversarial Privacy: A Data-driven Approach to Guaranteeing Privacy and Utility
-
批准号:1815361
-
项目类别:Standard Grant
-
资助金额:$30.0万
-
财政年份:2018
-
负责人:Lalitha Sankar
-
依托单位:
CAREER: Privacy-Guaranteed Distributed Interactions in Critical Infrastructure Networks
-
批准号:1350914
-
项目类别:Continuing Grant
-
资助金额:$45.5万
-
财政年份:2014
-
负责人:Lalitha Sankar
-
依托单位:
国内基金
海外基金
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