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What is Normal? A Big Data Observational Science Model of Anonymized Internet Traffic

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arxiv 2409.03111 v1 pith:W4JNROAC submitted 2024-09-04 cs.NI cs.CRcs.CYcs.SI

Jeremy Kepner , Hayden Jananthan , Michael Jones , William Arcand , David Bestor , William Bergeron , Daniel Burrill , Aydin Buluc
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Chansup Byun Timothy Davis Vijay Gadepally Daniel Grant Michael Houle Matthew Hubbell Piotr Luszczek Lauren Milechin Chasen Milner Guillermo Morales Andrew Morris Julie Mullen Ritesh Patel Alex Pentland Sandeep Pisharody Andrew Prout Albert Reuther Antonio Rosa Gabriel Wachman Charles Yee Peter Michaleas
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classification cs.NIcs.CRcs.CYcs.SI
keywords normalobservationalanonymizedhighinternetmodelssciencetraffic
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Understanding what is normal is a key aspect of protecting a domain. Other domains invest heavily in observational science to develop models of normal behavior to better detect anomalies. Recent advances in high performance graph libraries, such as the GraphBLAS, coupled with supercomputers enables processing of the trillions of observations required. We leverage this approach to synthesize low-parameter observational models of anonymized Internet traffic with a high regard for privacy.

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