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Title: 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}
}

Works referenced in this record:

Cybersecurity for Control Systems: A Process-Aware Perspective
journal, October 2016


Cyber-attack detection and accommodation algorithm for energy delivery systems
conference, August 2017


A machine learning approach for real-time reachability analysis
conference, September 2014