EAGER-DynamicData: A Hierarchical Approach to Dynamic Big Data Analysis in Power Infrastructure Security

University Of California-Riverside, Riverside CA

Investigators

Abstract

This research will address a dynamic big data problem that is of urgent national interest: the need for efficient methods to diagnose faults and attacks in critical interconnected infrastructures, such as electricity power networks. Additionally, this project will investigate new methodologies to extract knowledge from the complex streams of data that come from various sensors in infrastructure systems and the models of their behavior. Results and findings in this project will be validated via industry-accredited power system simulators, and will be useful to the power industry in enhancing the safety, stability, and security of essential power infrastructure. This project will promote multi-disciplinary research involving expertise in big data analysis, machine learning, security, power systems, and control systems. This research will provide a powerful bridge between theory and real-world applications while serving as a training platform for a diverse new generation of engineers at the University of California, Riverside, one of America's most ethnically diverse research-intensive institutions. This project will foster the use of multi-resolution data-driven methods for the detection and classification of anomalies in critical dynamical infrastructures, with focus on power networks. This project has three novel, innovative, and potentially transformative technical elements: (1) A comprehensive statistical model, as an alternative to existing physics-based models, using Dynamic Bayesian Networks and Conditional Random Fields to model complex infrastructures subject to failures and attacks; (2) A hierarchical detection and classification method based upon machine learning concepts to tame and leverage the vast amount and diversity of dynamic multi-resolution data collected by spatially distributed sensors; (3) A systematic method to train and inform data-driven methodologies from model-based and analytical knowledge that come from power systems and control theory to build scalable and performing detection and classification mechanisms in power infrastructure security.