Disclosed is an informed sampling technique for biasing a sample data set toward network data of interest for a particular application. Network data received at a network node (for example at a rate which is greater than a sampling rate for which the network node is configured) is chosen to be included in a sample set based on one or more predetermined signatures which are chosen to bias the sample set toward network data of interest for a particular application. For example, the sample set may be biased to include data of interest for fraud detection, spam detection, and intrusion detection. The particular signature(s) may be predefined by a user, or may be automatically generated by another network application. The invention may be implemented at various levels and nodes of a network. For example, the informed sampling may be implemented at a traffic monitoring function of a network router, a flow collector which receives network flow data from the router, or both.
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