MAP4: A Pragmatic Framework for In-Network Machine Learning Traffic Classification

Self-driving networks guided by machine-learning (ML) algorithms are the driving force for building networks of the future. ML is effective at making inferences about data that is too complex or too unpredictable for humans. The network softwarization enabled by a deep programmability approach opens...

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Veröffentlicht in:	IEEE eTransactions on network and service management 2022-12, Vol.19 (4), p.1-1
Hauptverfasser:	Xavier, Bruno Missi, Guimaraes, Rafael Silva, Comarela, Giovanni, Martinello, Magnos
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Classification tree analysis Data models Feature extraction Hardware in-networking Machine learning Random forests software-defined network Support vector machines
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