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Title: Data-driven model construction for industrial asset decision boundary classification

Abstract

In some embodiments, a system model construction platform may receive, from a system node data store, system node data associated with an industrial asset. The system model construction platform may automatically construct a data-driven, dynamic system model for the industrial asset based on the received system node data. A synthetic attack platform may then inject at least one synthetic attack into the data-driven, dynamic system model to create, for each of a plurality of monitoring nodes, a series of synthetic attack monitoring node values over time that represent simulated attacked operation of the industrial asset. The synthetic attack platform may store, in a synthetic attack space data source, the series of synthetic attack monitoring node values over time that represent simulated attacked operation of the industrial asset. This information may then be used, for example, along with normal operational data to construct a threat detection model for the industrial asset.

Inventors:
; ;
Issue Date:
Research Org.:
General Electric Co., Schenectady, NY (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1650970
Patent Number(s):
10671060
Application Number:
15/681,974
Assignee:
General Electric Company (Schenectady, NY)
Patent Classifications (CPCs):
G - PHYSICS G05 - CONTROLLING G05B - CONTROL OR REGULATING SYSTEMS IN GENERAL
H - ELECTRICITY H04 - ELECTRIC COMMUNICATION TECHNIQUE H04L - TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
DOE Contract Number:  
OE0000833
Resource Type:
Patent
Resource Relation:
Patent File Date: 08/21/2017
Country of Publication:
United States
Language:
English

Citation Formats

Abbaszadeh, Masoud, Mestha, Lalit Keshav, and Bushey, Cody Joe. Data-driven model construction for industrial asset decision boundary classification. United States: N. p., 2020. Web.
Abbaszadeh, Masoud, Mestha, Lalit Keshav, & Bushey, Cody Joe. Data-driven model construction for industrial asset decision boundary classification. United States.
Abbaszadeh, Masoud, Mestha, Lalit Keshav, and Bushey, Cody Joe. Tue . "Data-driven model construction for industrial asset decision boundary classification". United States. https://www.osti.gov/servlets/purl/1650970.
@article{osti_1650970,
title = {Data-driven model construction for industrial asset decision boundary classification},
author = {Abbaszadeh, Masoud and Mestha, Lalit Keshav and Bushey, Cody Joe},
abstractNote = {In some embodiments, a system model construction platform may receive, from a system node data store, system node data associated with an industrial asset. The system model construction platform may automatically construct a data-driven, dynamic system model for the industrial asset based on the received system node data. A synthetic attack platform may then inject at least one synthetic attack into the data-driven, dynamic system model to create, for each of a plurality of monitoring nodes, a series of synthetic attack monitoring node values over time that represent simulated attacked operation of the industrial asset. The synthetic attack platform may store, in a synthetic attack space data source, the series of synthetic attack monitoring node values over time that represent simulated attacked operation of the industrial asset. This information may then be used, for example, along with normal operational data to construct a threat detection model for the industrial asset.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {6}
}

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