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Model learning algorithms for anomaly detection in CERN control systems
The CERN automation infrastructure consists of over 600 heterogeneous industrial control systems with around 45 million deployed sensors, actuators and control objects. Therefore, it is evident that the monitoring of such huge system represents a challenging and complex task. This paper describes th...
Autores principales: | , , , , |
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Lenguaje: | eng |
Publicado: |
2018
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.18429/JACoW-ICALEPCS2017-TUCPA04 http://cds.cern.ch/record/2305655 |