Collaborative Research: SHIELD: Strategic Holistic Framework for Intrusion Prevention Using Multi-modal Data in Power Systems

合作研究：SHIELD：在电力系统中使用多模态数据进行入侵防御的战略整体框架

• 批准号: 2220346
• 负责人: Muhammad Ismail
• 金额: $ 37.5万
• 依托单位: Tennessee Technological University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2220346&HistoricalAwards=false
• 关键词: Collaborative; Research; SHIELD; Strategic; Holistic

This NSF project aims to strengthen the protection of national power grid against cyber-physical attacks. The project will bring transformative changes to the existing detection and prevention strategies that rely either on physical measurements or cyber data. This will be achieved through: (1) the fusion of cyber and physical power system data to better detect coordinated cyber-physical attacks and (2) the isolation of the impacted cyber-physical sections to contain the damage spread across the power system. The intellectual merits of the project include: (1) proposing novel methods to generate cyber and physical data that reflect the behavior of the power system under normal operation and coordinated cyber-physical attack scenarios, (2) proposing novel intrusion detection methods that fuse cyber and physical features from the power system, and (3) proposing novel intrusion prevention methods that isolate the impacted cyber and physical sections of the power system. The broader impacts of the project include: (1) defending critical infrastructures (power systems) against cyber-attacks via state-of-the-art detection and prevention strategies, (2) training of graduate and undergraduate students on cyber-physical system security through summer schools and STEM workshops, and (3) dissemination of research results to both industry and academic communities. Modern power systems are cyber-physical in nature. However, the existing attack detection strategies leverage either physical measurements or cyber data. Furthermore, the existing attack prevention strategies do not jointly isolate the impacted cyber and physical sections of the power system. To close this gap, this project proposes to develop SHIELD - Strategic Holistic framework for Intrusion prEvention using muLti-modal Data in power systems. SHIELD aims to offer better detection and prevention performance via: (1) optimal fusion of cyber and physical data for improved attack detection and (2) joint partitioning of the cyber and physical sections of the power systems for effective prevention results. To develop SHIELD into a practical architecture, the following research thrusts will be considered in this project: (1) Creation of comprehensive datasets of cyber and physical features of power systems under normal operation and coordinated cyber-attack scenarios, (2) Development of cyber-physical intrusion detection strategy via advanced deep machine learning techniques, and (3) Development of cyber-physical prevention strategy via optimal joint partitioning.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 项目旨在加强国家电网免受网络物理攻击的保护。该项目将为依赖物理测量或网络数据的现有检测和预防策略带来变革。这将通过以下方式实现：（1）融合网络和物理电力系统数据，以更好地检测协调的网络物理攻击；（2）隔离受影响的网络物理部分，以遏制整个电力系统的损害蔓延。该项目的智力优点包括：（1）提出生成网络和物理数据的新方法，这些数据反映了电力系统在正常运行和协调的网络物理攻击场景下的行为，（2）提出了融合网络和物理数据的新型入侵检测方法。 (3) 提出新颖的入侵防御方法，隔离电力系统受影响的网络和物理部分。该项目的更广泛影响包括：(1) 通过最先进的检测和预防策略保护关键基础设施（电力系统）免受网络攻击，(2) 对研究生和本科生进行网络物理系统安全培训通过暑期学校和 STEM 研讨会，以及 (3) 向行业和学术界传播研究成果。现代电力系统本质上是网络物理的。然而，现有的攻击检测策略利用物理测量或网络数据。此外，现有的攻击防御策略并未共同隔离电力系统受影响的网络和物理部分。为了弥补这一差距，该项目建议开发 SHIELD - 在电力系统中使用多模态数据进行入侵预防的战略整体框架。 SHIELD 旨在通过以下方式提供更好的检测和预防性能：(1) 网络和物理数据的最佳融合，以改进攻击检测；(2) 电力系统的网络和物理部分的联合分区，以实现有效的预防结果。为了将 SHIELD 发展成为实用的架构，该项目将考虑以下研究重点：（1）创建正常运行和协调网络攻击场景下电力系统网络和物理特征的综合数据集，（2）开发网络-通过先进的深度机器学习技术的物理入侵检测策略，以及（3）通过最佳联合分区开发网络物理防御策略。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评估进行评估，认为值得支持影响审查 标准。