Hybrid feature-driven learning system for abnormality detection and localization
Patent
·
OSTI ID:1859959
A cyber-physical system may have a plurality of monitoring nodes each generating a series of current monitoring node values over time representing current operation of the system. A data-driven features extraction computer platform may receive the series of current monitoring node values and generate current data-driven feature vectors based on the series of current monitoring node values. A residual features extraction computer platform may receive the series of current monitoring node values, execute a system model and utilize a stochastic filter to determine current residual values, and generate current residual-driven feature vectors based on the current residual values. An abnormal detection platform may then receive the current data-driven and residual-driven feature vectors and compare the current data-driven and residual-driven feature vectors with at least one decision boundary associated with an abnormal detection model. An abnormal alert signal may then be transmitted when appropriate based on a result of said comparison.
- Research Organization:
- General Electric Co., Schenectady, NY (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- OE0000902
- Assignee:
- General Electric Company (Schenectady, NY)
- Patent Number(s):
- 11,146,579
- Application Number:
- 16/138,408
- OSTI ID:
- 1859959
- Country of Publication:
- United States
- Language:
- English
Attack Detection and Identification in Cyber-Physical Systems
|
journal | November 2013 |
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