Jeremy Kepner, Hayden Jananthan, Michael Jones, William Arcand, David Bestor, William Bergeron, Daniel Burrill, Aydin Buluc, 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
{"title":"What is Normal? A Big Data Observational Science Model of Anonymized Internet Traffic","authors":"Jeremy Kepner, Hayden Jananthan, Michael Jones, William Arcand, David Bestor, William Bergeron, Daniel Burrill, Aydin Buluc, 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","doi":"arxiv-2409.03111","DOIUrl":null,"url":null,"abstract":"Understanding what is normal is a key aspect of protecting a domain. Other\ndomains invest heavily in observational science to develop models of normal\nbehavior to better detect anomalies. Recent advances in high performance graph\nlibraries, such as the GraphBLAS, coupled with supercomputers enables\nprocessing of the trillions of observations required. We leverage this approach\nto synthesize low-parameter observational models of anonymized Internet traffic\nwith a high regard for privacy.","PeriodicalId":501032,"journal":{"name":"arXiv - CS - Social and Information Networks","volume":"25 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Social and Information Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.03111","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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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