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Detecting anomalous behavior in network traffic is a major challenge due tothe volume and velocity of network traffic. For example, a 10 Gigabit Ethernetconnection can generate over 50 MB/s of packet headers. For global networkproviders, this challenge can be amplified by many orders of magnitude.Development of novel computer network traffic analytics requires: high levelprogramming environments, massive amount of packet capture (PCAP) data, anddiverse data products for "at scale" algorithm pipeline development. D4M(Dynamic Distributed Dimensional Data Model) combines the power of sparselinear algebra, associative arrays, parallel processing, and distributeddatabases (such as SciDB and Apache Accumulo) to provide a scalable data andcomputation system that addresses the big data problems associated with networkanalytics development. Combining D4M with the MIT SuperCloud manycoreprocessors and parallel storage system enables network analysts tointeractively process massive amounts of data in minutes. To demonstrate thesecapabilities, we have implemented a representative analytics pipeline in D4Mand benchmarked it on 96 hours of Gigabit PCAP data with MIT SuperCloud. Theentire pipeline from uncompressing the raw files to database ingest wasimplemented in 135 lines of D4M code and achieved speedups of over 20,000.

Hyperscaling Internet Graph Analysis with D4M on the MIT SuperCloud