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

NSF CAREER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。该项目将利用广泛部署的智能设备，将现有依赖有限网络层安全机制的批量传输系统操作转变为网络层和物理层的主动纵深防御方法。该项目的智力优势包括开发新的优化、图论、低秩矩阵理论和基于机器学习的方法，用于移动目标防御设备的优化规划和操作，快速检测、准确识别和强大的网络数据完整性攻击缓解。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，并为包括中学生和高中生在内的多元化学习社区做好准备，使其具备应对未来电网安全挑战所需的知识和技能。该项目的成功完成将为电力系统运营商提供新的工具，以增强态势感知能力，更好地保护电网免受网络数据攻击。MTD是一个新兴的概念，最初是为计算机和通信网络引入的。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受到物理破坏，则会在物理层内触发不良后果。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。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的数据完整性攻击。提出的MTD方法具有三个主要的技术创新：1)最小生成树支持的规划方案，在考虑系统经济和可靠性指标的同时最大限度地提高MTD检测效率；2)在可扩展电压稳定方法的约束下，提出了一种新的交流最优潮流运行框架，以保证MTD的隐蔽性和检测性能；3)基于MTD方法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。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.