A method for classifying network traffic, including (1) processing a first working set portion of a flow batch for a first iteration by dividing the first working set portion into clusters and filtering a cluster by (i) identifying a first server port as most frequently occurring comparing to all other server ports in the cluster, (ii) in response to determining that a first frequency of occurrence of the first server port in the cluster exceeds a pre-determined threshold: (a) identifying the cluster as a dominatedPort cluster, (b) removing the cluster from the first working set portion to generate a remainder as a second working set portion, and (c) removing, from the cluster to be added to the second working set portion, one or more flows having different server port than the first server port, and (2) processing the second working set portion for a second iteration.

Self-learning classifier for internet traffic / Ram, Keralapura; Mellia, Marco; Grimaudo, Luigi. - (2014).

Self-learning classifier for internet traffic

MELLIA, Marco;GRIMAUDO, LUIGI
2014

Abstract

A method for classifying network traffic, including (1) processing a first working set portion of a flow batch for a first iteration by dividing the first working set portion into clusters and filtering a cluster by (i) identifying a first server port as most frequently occurring comparing to all other server ports in the cluster, (ii) in response to determining that a first frequency of occurrence of the first server port in the cluster exceeds a pre-determined threshold: (a) identifying the cluster as a dominatedPort cluster, (b) removing the cluster from the first working set portion to generate a remainder as a second working set portion, and (c) removing, from the cluster to be added to the second working set portion, one or more flows having different server port than the first server port, and (2) processing the second working set portion for a second iteration.
2014
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2540288
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