Concurrent Learning Cyber-Physical Framework for Resilient Electric Power System (CyberPREPS)
Sponsors: Department of Energy (DOE)
Team: FSU (Lead), GE Vernova, University of North Carolina Charlotte (UNCC), New York Power Authority (NYPA), Nhu Energy Inc (NEi)
Status: Active
Leverages the cyber-physical nature of the energy delivery transmission and distribution process to develop a suite of concurrent learning resilient algorithms.
Cyber Physical Resiliency (CyPr)
Sponsors: The Department of Energy (DOE)
Team: FSU, GE Vernova (Lead), Idaho National Lab
Status: Active
Develops a new level of resiliency for natural gas compressor stations. The project aims to allow continued operation even in the event of a cyber attack, focusing on the cyber-physical aspects of industrial control systems within the compressor stations.
Resilient Energy Delivery Control Systems (REDCS)
Sponsors: The Department of Energy (DOE)
Team: FSU, GE Vernova (Lead), Baker Hughes, Intel
Status: Complete
This project develops REDCS, consisting of sophisticated algorithm modules hosted on a secure computer system with the goal of maintaining operation at a safe state while a cyber-attack is underway. The proposed solution, enabled by the latest advancements in Machine Learning and Control Theory, will allow for continued operation of a natural gas pipeline during a cyber-attack by monitoring related physics to quickly detect attacks, isolate impacted nodes, predict the onset of anomalies, and self-heal to mitigate the impacts.
Cyber Simulator for CPS
Sponsors: DARPA
Team: FSU, GE Vernona (Lead), NREL
Status: Complete
Develops integrated simulation environment for realistic cybersecurity training of advanced DoD CPS
Concurrent-Learning Resilient Cyber-Physical Systems
Sponsors: FSU Seed funding via the First Year Assistant Professor Award (FYAP)
Status: Complete
The aim of this project is to leverage the cyber-physical nature of critical infrastructures to develop a suite of concurrent learning resilient algorithms. The developed algorithms will seamlessly merge data-driven machine learning models, for the cyber layer, with domain knowledge physics-based models, for the physical layer, to simultaneously achieve high accuracy and high generalizability for detecting, localizing, and neutralizing both known and unknown extreme events. This will enable cyber-physical critical infrastructures to survive a cyber incident while sustaining critical functions.