Virtual sensor supervised learning for cyber-attack neutralization
Abstract
An industrial asset may have monitoring nodes that generate current monitoring node values. A dynamic, resilient estimator may split a temporal monitoring node space into normal and one or more abnormal subspaces associated with different kinds of attack vectors. According to some embodiments, a neutralization model is constructed and trained for each attack vector using supervised learning and the associated abnormal subspace. In other embodiments, a single model is created using out-of-range values for abnormal monitoring nodes. Responsive to an indication of a particular abnormal monitoring node or nodes, the system may automatically invoke the appropriate neutralization model to determine estimated values of the particular abnormal monitoring node or nodes (e.g., by selecting the correct model or using out-of-range values). The series of current monitoring node values from the abnormal monitoring node or nodes may then be replaced with the estimated values.
- Inventors:
- Issue Date:
- Research Org.:
- General Electric Co., Schenectady, NY (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 2222146
- Patent Number(s):
- 11729190
- Application Number:
- 16/666,807
- Assignee:
- General Electric Company (Schenectady, NY)
- DOE Contract Number:
- OE0000833
- Resource Type:
- Patent
- Resource Relation:
- Patent File Date: 10/29/2019
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING
Citation Formats
Roychowdhury, Subhrajit, Abbaszadeh, Masoud, and Dokucu, Mustafa Tekin. Virtual sensor supervised learning for cyber-attack neutralization. United States: N. p., 2023.
Web.
Roychowdhury, Subhrajit, Abbaszadeh, Masoud, & Dokucu, Mustafa Tekin. Virtual sensor supervised learning for cyber-attack neutralization. United States.
Roychowdhury, Subhrajit, Abbaszadeh, Masoud, and Dokucu, Mustafa Tekin. Tue .
"Virtual sensor supervised learning for cyber-attack neutralization". United States. https://www.osti.gov/servlets/purl/2222146.
@article{osti_2222146,
title = {Virtual sensor supervised learning for cyber-attack neutralization},
author = {Roychowdhury, Subhrajit and Abbaszadeh, Masoud and Dokucu, Mustafa Tekin},
abstractNote = {An industrial asset may have monitoring nodes that generate current monitoring node values. A dynamic, resilient estimator may split a temporal monitoring node space into normal and one or more abnormal subspaces associated with different kinds of attack vectors. According to some embodiments, a neutralization model is constructed and trained for each attack vector using supervised learning and the associated abnormal subspace. In other embodiments, a single model is created using out-of-range values for abnormal monitoring nodes. Responsive to an indication of a particular abnormal monitoring node or nodes, the system may automatically invoke the appropriate neutralization model to determine estimated values of the particular abnormal monitoring node or nodes (e.g., by selecting the correct model or using out-of-range values). The series of current monitoring node values from the abnormal monitoring node or nodes may then be replaced with the estimated values.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2023},
month = {8}
}
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