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Internet analysis is a major challenge due to the volume and rate of networktraffic. In lieu of analyzing traffic as raw packets, network analysts oftenrely on compressed network flows (netflows) that contain the start time, stoptime, source, destination, and number of packets in each direction. However,many traffic analyses benefit from temporal aggregation of multiplesimultaneous netflows, which can be computationally challenging. To alleviatethis concern, a novel netflow compression and resampling method has beendeveloped leveraging GraphBLAS hyperspace traffic matrices that preserveanonymization while enabling subrange analysis. Standard multitemporal spatialanalyses are then performed on each subrange to generate detailed statisticalaggregates of the source packets, source fan-out, unique links, destinationfan-in, and destination packets of each subrange which can then be used forbackground modeling and anomaly detection. A simple file format based onGraphBLAS sparse matrices is developed for storing these statisticalaggregates. This method is scale tested on the MIT SuperCloud using a 50trillion packet netflow corpus from several hundred sites collected overseveral months. The resulting compression achieved is significant (<0.1 bit perpacket) enabling extremely large netflow analyses to be stored and transported.The single node parallel performance is analyzed in terms of both processorsand threads showing that a single node can perform hundreds of simultaneousanalyses at over a million packets/sec (roughly equivalent to a 10 Gigabitlink).

Hypersparse Network Flow Analysis of Packets with GraphBLAS