Spatial Temporal Analysis of 40,000,000,000,000 Internet Darkspace Packets

Jeremy Kepner, Michael Jones, Daniel Andersen, Aydin Buluc, Chansup Byun, K. Claffy, Timothy Davis, William Arcand, Jonathan Bernays, David Bestor, William Bergeron, Vijay Gadepally, Micheal Houle, Matthew Hubbell, Anna Klein, Chad Meiners, Lauren Milechin, Julie Mullen, Sandeep Pisharody, Andrew ProutAlbert Reuther, Antonio Rosa, Siddharth Samsi, Doug Stetson, Adam Tse, Charles Yee, Peter Michaleas

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations


The Internet has never been more important to our society, and understanding the behavior of the Internet is essential. The Center for Applied Internet Data Analysis (CAIDA) Telescope observes a continuous stream of packets from an unsolicited darkspace representing 1/256 of the Internet. During 2019 and 2020 over 40,000,000,000,000 unique packets were collected representing the largest ever assembled public corpus of Internet traffic. Using the combined resources of the Supercomputing Centers at UC San Diego, Lawrence Berkeley National Laboratory, and MIT, the spatial temporal structure of anonymized source-destination pairs from the CAIDA Telescope data has been analyzed with GraphBLAS hierarchical hyper-sparse matrices. These analyses provide unique insight on this unsolicited Internet darkspace traffic with the discovery of many previously unseen scaling relations. The data show a significant sustained increase in unsolicited traffic corresponding to the start of the COVID19 pandemic, but relatively little change in the underlying scaling relations associated with unique sources, source fan-outs, unique links, destination fan-ins, and unique destinations. This work provides a demonstration of the practical feasibility and benefit of the safe collection and analysis of significant quantities of anonymized Internet traffic.

Original languageEnglish
Title of host publication2021 IEEE High Performance Extreme Computing Conference, HPEC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665423694
StatePublished - 2021
Externally publishedYes
Event2021 IEEE High Performance Extreme Computing Conference, HPEC 2021 - Virtual, Online, United States
Duration: Sep 20 2021Sep 24 2021

Publication series

Name2021 IEEE High Performance Extreme Computing Conference, HPEC 2021


Conference2021 IEEE High Performance Extreme Computing Conference, HPEC 2021
Country/TerritoryUnited States
CityVirtual, Online


  • Internet modeling
  • hypersparse matrices
  • packet capture
  • power-law networks
  • streaming graphs


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