Dynamic concurrent learning method to neutralize cyber attacks and faults for industrial asset monitoring nodes
Abstract
Input signals may be received from monitoring nodes of the industrial asset, each input signal comprising time series data representing current operation. A neutralization engine may transform the input signals into feature vectors in feature space, each feature vector being associated with one of a plurality of overlapping batches of received input signals. A dynamic decision boundary may be generated based on the set of feature vectors, and an abnormal state of the asset may be detected based on the set of feature vectors and a predetermined static decision boundary. An estimated neutralized value for each abnormal feature value may be calculated based on the dynamic decision boundary and the static decision boundary such that a future set of feature vectors will be moved with respect to the static decision boundary. An inverse transform of each estimated neutralized value may be performed to generate neutralized signals comprising time series data that are output.
- Inventors:
- Issue Date:
- Research Org.:
- General Electric Co., Schenectady, NY (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1735093
- Patent Number(s):
- 10728282
- Application Number:
- 15/986,996
- Assignee:
- General Electric Company (Schenectady, NY)
- Patent Classifications (CPCs):
-
G - PHYSICS G06 - COMPUTING G06N - COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
Y - NEW / CROSS SECTIONAL TECHNOLOGIES Y04 - INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS Y04S - SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- DOE Contract Number:
- OE0000833
- Resource Type:
- Patent
- Resource Relation:
- Patent File Date: 05/23/2018
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Mestha, Lalit Keshav, Anubi, Olugbenga, and Achanta, Hema Kumari. Dynamic concurrent learning method to neutralize cyber attacks and faults for industrial asset monitoring nodes. United States: N. p., 2020.
Web.
Mestha, Lalit Keshav, Anubi, Olugbenga, & Achanta, Hema Kumari. Dynamic concurrent learning method to neutralize cyber attacks and faults for industrial asset monitoring nodes. United States.
Mestha, Lalit Keshav, Anubi, Olugbenga, and Achanta, Hema Kumari. Tue .
"Dynamic concurrent learning method to neutralize cyber attacks and faults for industrial asset monitoring nodes". United States. https://www.osti.gov/servlets/purl/1735093.
@article{osti_1735093,
title = {Dynamic concurrent learning method to neutralize cyber attacks and faults for industrial asset monitoring nodes},
author = {Mestha, Lalit Keshav and Anubi, Olugbenga and Achanta, Hema Kumari},
abstractNote = {Input signals may be received from monitoring nodes of the industrial asset, each input signal comprising time series data representing current operation. A neutralization engine may transform the input signals into feature vectors in feature space, each feature vector being associated with one of a plurality of overlapping batches of received input signals. A dynamic decision boundary may be generated based on the set of feature vectors, and an abnormal state of the asset may be detected based on the set of feature vectors and a predetermined static decision boundary. An estimated neutralized value for each abnormal feature value may be calculated based on the dynamic decision boundary and the static decision boundary such that a future set of feature vectors will be moved with respect to the static decision boundary. An inverse transform of each estimated neutralized value may be performed to generate neutralized signals comprising time series data that are output.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {7}
}
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