CAREER: Towards attack-resilient cyber-physical smart grids: moving target defense for data integrity attack detection, identification and mitigation

职业：迈向抗攻击的网络物理智能电网：用于数据完整性攻击检测、识别和缓解的移动目标防御

基本信息

批准号：
2146156
负责人：
Hongyu Wu
金额：
$ 50万
依托单位：
Kansas State University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-03-01 至 2027-02-28
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2146156&HistoricalAwards=false
关键词：
CAREER Towards attack resilient cyber

项目摘要

This NSF CAREER project aims to provide a theoretical foundation and design guiding principles that will unlock the full potential of moving target defense (MTD) approaches and significantly enhance the resiliency of cyber-physical power grids under cyber data attacks. The project will transform existing bulk transmission system operations that rely on limited cyber-layer security mechanisms to proactive defense-in-depth approaches in both the cyber and physical layers using widely-deployed smart devices. The intellectual merits of the project include developing novel optimization, graph theory, low-rank matrix theory, and machine learning-based methods for optimal planning and operation of moving target defense devices, rapid detection, accurate identification, and robust mitigation of cyber data integrity attacks. The broader impacts of the project include promoting public awareness and understanding of smart grid cybersecurity, contributing to power engineering education, and preparing a diverse learning community, including middle and high school students, with requisite knowledge and skillsets to tackle future power grid security challenges. The successful completion of this project will provide power system operators with new tools to enhance situational awareness and better defend the power grid against cyber data attacks.MTD is an emerging concept originally introduced for computer and communication networks. Existing MTD approaches are limited to the cyber layer of a cyber-physical system. However, if field devices or internal communication networks are physically compromised, adverse consequences are trigged within the physical layer. Therefore, the cyber-layer MTD alone is inadequate for securing real-world power grids with significant attack surfaces. The goal of this CAREER project is to develop and validate physical-layer MTD approaches to detect, identify, and mitigate data integrity attacks by strong adversaries with state-of-the-art machine learning capabilities. The proposed MTD approaches feature three major technical innovations: 1) A minimum spanning tree-enabled planning scheme that maximizes MTD detection effectiveness while considering system economic and reliability metrics; 2) A novel alternating current optimal power flow operational framework, constrained by scalable voltage stability approaches, to ensure the MTD hiddenness and detection performance; and 3) A low-rank matrix decomposition method assisted by MTD approaches that radically improves the attack identification speed and measurement recovery accuracy.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.

NSF职业项目旨在提供理论基础和设计指导原则，该原则将释放移动目标防御（MTD）方法的全部潜力，并显着提高网络数据攻击下网络物理电网的弹性。该项目将改变现有的批量传输系统操作，这些操作依靠有限的网络层安全机制，以使用广泛的智能设备在网络和物理层中主动防御方法。该项目的智力优点包括开发新颖的优化，图理论，低级别矩阵理论以及基于机器学习的方法，用于最佳计划和运行目标防御设备，快速检测，准确的识别以及对网络数据完整性攻击的强大缓解。该项目的更广泛的影响包括提高公众意识和对智能电网网络安全的了解，为动力工程教育做出贡献，并为包括中学和高中生在内的各种学习社区做好准备，并具有必要的知识和技能，以应对未来的电力网络安全挑战。该项目的成功完成将为电力系统运营商提供新的工具，以增强情境意识，并更好地捍卫电网，以应对网络数据攻击。MTD是最初针对计算机和通信网络引入的新兴概念。现有的MTD方法仅限于网络物理系统的网络层。但是，如果现场设备或内部通信网络在物理上受到损害，则在物理层中会触发不利后果。因此，仅网络层MTD就不足以确保具有明显攻击表面的现实世界电网。该职业项目的目的是开发和验证物理层MTD方法，以通过具有最先进的机器学习能力来检测，识别和减轻强大对手的数据完整性攻击。拟议的MTD方法具有三个主要的技术创新：1）最低跨越树木的规划计划，该计划在考虑系统经济和可靠性指标的同时，最大程度地提高了MTD检测有效性； 2）一种新型交替的当前最佳功率流动框架，受可扩展电压稳定性方法约束，以确保MTD的隐藏性和检测性能； 3）一种由MTD方法辅助的低级矩阵分解方法从根本上提高了攻击识别速度和测量恢复精度。该奖项反映了NSF的法定任务，并被认为值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来获得支持。