Framework for joint learning of network traffic representations and traffic classifiers
Abstract:
In one embodiment, a device in a network receives traffic data associated with a particular communication channel between two or more nodes in the network. The device generates a mean map by employing kernel embedding of distributions to the traffic data. The device forms a representation of the communication channel by identifying a set of lattice points that approximate the mean map. The device generates a traffic classifier using the representation of the communication channel. The device uses machine learning to jointly identify the set of lattice points and one or more parameters of the traffic classifier. The device causes the traffic classifier to analyze network traffic sent via the communication channel.
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