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

Tennessee Technological University, Cookeville TN

Investigators

Abstract

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.