Securing networked cyber-physical systems, such as an electric power grid, has emerged as paramount to national and economic security. Adversaries exploit the vulnerabilities in both cyber and physical domains and deploy stealthy multistage cyberattacks to disrupt the smart grid’s reliable operation. This project will leverage physics-based Machine Learning and Deep Learning algorithms to build an attack-resilient cyber-physical smart grid infrastructure, aiming for advanced levels of cyber-physical system security for the smart grid while simultaneously enhancing the system resiliency and reliability. The project leverages physics-based Machine Learning and Deep Learning algorithms to detect data-integrity, denial-of-service, and distributed denial-of-service cyberattacks. The project likewise pursues multidisciplinary education of graduate and undergraduate students, including underrepresented minorities.
The project’s two primary goals are -- i) Detecting anomalies on both measurement and control signals using physics-based machine learning algorithms against data-integrity cyber attacks for cyber-physical smart grid applications, and subsequently develop suitable mitigation strategies for system operators. ii) Reconstructing the missing data due to denial-of-service and distributed denial-of-service cyber attacks under both normal and perturbed grid conditions using physics-based deep learning algorithms to ensure reliable and continuous data frames provided for cyber-physical smart grid applications. The proposed physics-based machine learning and deep learning attack-resiliency algorithms will be designed, trained, tested, and evaluated using hardware-in-the-loop cloud infrastructure. The project will leverages collaboration with utility companies for training and inferring practical machine learning and deep learning models.
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.
