Agglomerative Clustering of Network Traffic Based on Various Approaches to Determining the Distance Matrix

We are presenting a real-time traffic flow classification model for maintaining QoS in dynamic networks such as Software Defined Networks (SDN). In previous works, we managed to achieve high accuracy (90-95%) on the database of flows known for the model using Machine Learning (Supervised Learning) m...

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Bibliographische Detailangaben

Hauptverfasser:	Deart, Vladimir, Mankov, Vladimir, Krasnova, Irina
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	agglomerative clustering distance matrix euclidean distance extremely randomized trees Load modeling machine learning manhattan distance Quality of service random forest Random forests random trees embedding Software defined networking Supervised learning traffic classification Vegetation
Online-Zugang:	Volltext
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