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Title: Integrated Cyber/Physical Grid Resiliency Modeling.

Abstract

This project explored coupling modeling and analysis methods from multiple domains to address complex hybrid (cyber and physical) attacks on mission critical infrastructure. Robust methods to integrate these complex systems are necessary to enable large trade-space exploration including dynamic and evolving cyber threats and mitigations. Reinforcement learning employing deep neural networks, as in the AlphaGo Zero solution, was used to identify "best" (or approximately optimal) resilience strategies for operation of a cyber/physical grid model. A prototype platform was developed and the machine learning (ML) algorithm was made to play itself in a game of 'Hurt the Grid'. This proof of concept shows that machine learning optimization can help us understand and control complex, multi-dimensional grid space. A simple, yet high-fidelity model proves that the data have spatial correlation which is necessary for any optimization or control. Our prototype analysis showed that the reinforcement learning successfully improved adversary and defender knowledge to manipulate the grid. When expanded to more representative models, this exact type of machine learning will inform grid operations and defense - supporting mitigation development to defend the grid from complex cyber attacks! This same research can be expanded to similar complex domains.

Authors:
; ; ; ; ; ; ; ; ;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sandia National Laboratories, Livermore, CA
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1482777
Report Number(s):
SAND2018-12934
669759
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English

Citation Formats

Dawson, Lon Andrew, Verzi, Stephen Joseph, Levin, Drew, Melander, Darryl J., Sorensen, Asael Hal, Cauthen, Katherine Regina, Wilches Bernal, Felipe, Berg, Timothy M., Lavrova, Olga, and Guttromson, Ross. Integrated Cyber/Physical Grid Resiliency Modeling.. United States: N. p., 2018. Web. doi:10.2172/1482777.
Dawson, Lon Andrew, Verzi, Stephen Joseph, Levin, Drew, Melander, Darryl J., Sorensen, Asael Hal, Cauthen, Katherine Regina, Wilches Bernal, Felipe, Berg, Timothy M., Lavrova, Olga, & Guttromson, Ross. Integrated Cyber/Physical Grid Resiliency Modeling.. United States. doi:10.2172/1482777.
Dawson, Lon Andrew, Verzi, Stephen Joseph, Levin, Drew, Melander, Darryl J., Sorensen, Asael Hal, Cauthen, Katherine Regina, Wilches Bernal, Felipe, Berg, Timothy M., Lavrova, Olga, and Guttromson, Ross. Thu . "Integrated Cyber/Physical Grid Resiliency Modeling.". United States. doi:10.2172/1482777. https://www.osti.gov/servlets/purl/1482777.
@article{osti_1482777,
title = {Integrated Cyber/Physical Grid Resiliency Modeling.},
author = {Dawson, Lon Andrew and Verzi, Stephen Joseph and Levin, Drew and Melander, Darryl J. and Sorensen, Asael Hal and Cauthen, Katherine Regina and Wilches Bernal, Felipe and Berg, Timothy M. and Lavrova, Olga and Guttromson, Ross},
abstractNote = {This project explored coupling modeling and analysis methods from multiple domains to address complex hybrid (cyber and physical) attacks on mission critical infrastructure. Robust methods to integrate these complex systems are necessary to enable large trade-space exploration including dynamic and evolving cyber threats and mitigations. Reinforcement learning employing deep neural networks, as in the AlphaGo Zero solution, was used to identify "best" (or approximately optimal) resilience strategies for operation of a cyber/physical grid model. A prototype platform was developed and the machine learning (ML) algorithm was made to play itself in a game of 'Hurt the Grid'. This proof of concept shows that machine learning optimization can help us understand and control complex, multi-dimensional grid space. A simple, yet high-fidelity model proves that the data have spatial correlation which is necessary for any optimization or control. Our prototype analysis showed that the reinforcement learning successfully improved adversary and defender knowledge to manipulate the grid. When expanded to more representative models, this exact type of machine learning will inform grid operations and defense - supporting mitigation development to defend the grid from complex cyber attacks! This same research can be expanded to similar complex domains.},
doi = {10.2172/1482777},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {11}
}