PI: Qinghua Li, University of Arkansas
Co-PI: Dirk Reiners, University of Arkansas, Little Rock
Co-PI: Carolina Cruz-Neira, University of Arkansas, Little Rock
Patching security vulnerabilities continues to be a heavily manual intensive process in the energy sector. Energy companies spend a tremendous amount of human resources digging through vulnerability bulletins, determining and validating mitigating activities. To address this issue, this project will research and develop a tool that automatically learns and predicts human operator’s decisions about the patches/remediation actions applied to the energy company’s security vulnerabilities so that the operator does not have to manually decide on every patch/remediation action. This tool will greatly reduce the human efforts spent on analyzing which patches/remediation actions should be applied to a company. This project also involves collaborations with AECC, Westar Energy, EPRI, Leidos, Foxguard Solutions, and TDi Technologies in monthly calls.