Machine Learning Methods for Attack Detection in the Smart Grid

Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2016-08, Vol.27 (8), p.1773-1786
Hauptverfasser:	Ozay, Mete, Esnaola, Inaki, Yarman Vural, Fatos Tunay, Kulkarni, Sanjeev R., Poor, H. Vincent
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Attack detection classification Distance education Distance learning Kernel Learning Learning systems Machine learning Machine learning algorithms Mathematical analysis Neural networks phase transition Prediction algorithms Smart grid smart grid security Smart grids sparse optimization State vectors Statistical learning Vectors
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!