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.

该 NSF 职业项目旨在提供理论基础和设计指导原则，以释放移动目标防御（MTD）方法的全部潜力，并显着增强网络物理电网在网络数据攻击下的弹性。该项目将把依赖有限网络层安全机制的现有大容量传输系统操作转变为使用广泛部署的智能设备在网络和物理层主动进行深度防御的方法。该项目的智力优势包括开发新颖的优化、图论、低秩矩阵理论和基于机器学习的方法，用于移动目标防御装置的优化规划和操作、快速检测、准确识别和网络数据完整性的稳健缓解攻击。该项目的更广泛影响包括提高公众对智能电网网络安全的认识和理解，为电力工程教育做出贡献，并为包括初高中生在内的多元化学习社区做好应对未来电网安全挑战所需的知识和技能的准备。该项目的成功完成将为电力系统运营商提供新的工具，以增强态势感知并更好地保护电网免受网络数据攻击。MTD是一个最初为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。然而，如果现场设备或内部通信网络在物理上受到损害，则会在物理层内引发不利后果。因此，仅网络层 MTD 不足以保护具有重大攻击面的现实世界电网。该 CAREER 项目的目标是开发和验证物理层 MTD 方法，以检测、识别和减轻具有最先进机器学习能力的强大对手发起的数据完整性攻击。所提出的MTD方法具有三项主要技术创新：1）最小生成树支持的规划方案，在考虑系统经济性和可靠性指标的同时最大化MTD检测有效性； 2）一种新颖的交流最优潮流运算框架，以可扩展的电压稳定方法为约束，以确保MTD隐藏性和检测性能； 3）MTD方法辅助的低秩矩阵分解方法，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的法定使命，并通过使用基金会的智力价值和更广泛的影响审查进行评估，认为值得支持标准。