TY - GEN
T1 - Spatial Temporal Analysis of 40,000,000,000,000 Internet Darkspace Packets
AU - Kepner, Jeremy
AU - Jones, Michael
AU - Andersen, Daniel
AU - Buluc, Aydin
AU - Byun, Chansup
AU - Claffy, K.
AU - Davis, Timothy
AU - Arcand, William
AU - Bernays, Jonathan
AU - Bestor, David
AU - Bergeron, William
AU - Gadepally, Vijay
AU - Houle, Micheal
AU - Hubbell, Matthew
AU - Klein, Anna
AU - Meiners, Chad
AU - Milechin, Lauren
AU - Mullen, Julie
AU - Pisharody, Sandeep
AU - Prout, Andrew
AU - Reuther, Albert
AU - Rosa, Antonio
AU - Samsi, Siddharth
AU - Stetson, Doug
AU - Tse, Adam
AU - Yee, Charles
AU - Michaleas, Peter
N1 - Funding Information:
This material is based upon work supported by the Assistant Secretary of Defense for Research and Engineering under Air Force Contract No. FA8702-15-D-0001, National Science Foundation CCF-1533644, and United States Air Force Research Laboratory Cooperative Agreement Number FA8750-19-2-1000. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Assistant Secretary of Defense for Research and Engineering, the National Science Foundation, or the United States Air Force. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Internet modeling
KW - hypersparse matrices
KW - packet capture
KW - power-law networks
KW - streaming graphs
UR - http://www.scopus.com/inward/record.url?scp=85123479298&partnerID=8YFLogxK
U2 - 10.1109/HPEC49654.2021.9622790
DO - 10.1109/HPEC49654.2021.9622790
M3 - Conference contribution
AN - SCOPUS:85123479298
T3 - 2021 IEEE High Performance Extreme Computing Conference, HPEC 2021
BT - 2021 IEEE High Performance Extreme Computing Conference, HPEC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 20 September 2021 through 24 September 2021
ER -