Anonymized Network Sensing Graph Challenge
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arXiv
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| Format: | Preprint |
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2024
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| author | Jananthan, Hayden Jones, Michael Arcand, William Bestor, David Bergeron, William Burrill, Daniel Buluc, Aydin Byun, Chansup Davis, Timothy Gadepally, Vijay Grant, Daniel Houle, Michael Hubbell, Matthew Luszczek, Piotr Michaleas, Peter Milechin, Lauren Milner, Chasen Morales, Guillermo Morris, Andrew Mullen, Julie Patel, Ritesh Pentland, Alex Pisharody, Sandeep Prout, Andrew Reuther, Albert Rosa, Antonio Wachman, Gabriel Yee, Charles Kepner, Jeremy |
| author_facet | Jananthan, Hayden Jones, Michael Arcand, William Bestor, David Bergeron, William Burrill, Daniel Buluc, Aydin Byun, Chansup Davis, Timothy Gadepally, Vijay Grant, Daniel Houle, Michael Hubbell, Matthew Luszczek, Piotr Michaleas, Peter Milechin, Lauren Milner, Chasen Morales, Guillermo Morris, Andrew Mullen, Julie Patel, Ritesh Pentland, Alex Pisharody, Sandeep Prout, Andrew Reuther, Albert Rosa, Antonio Wachman, Gabriel Yee, Charles Kepner, Jeremy |
| contents | The MIT/IEEE/Amazon GraphChallenge encourages community approaches to developing new solutions for analyzing graphs and sparse data derived from social media, sensor feeds, and scientific data to discover relationships between events as they unfold in the field. The anonymized network sensing Graph Challenge seeks to enable large, open, community-based approaches to protecting networks. Many large-scale networking problems can only be solved with community access to very broad data sets with the highest regard for privacy and strong community buy-in. Such approaches often require community-based data sharing. In the broader networking community (commercial, federal, and academia) anonymized source-to-destination traffic matrices with standard data sharing agreements have emerged as a data product that can meet many of these requirements. This challenge provides an opportunity to highlight novel approaches for optimizing the construction and analysis of anonymized traffic matrices using over 100 billion network packets derived from the largest Internet telescope in the world (CAIDA). This challenge specifies the anonymization, construction, and analysis of these traffic matrices. A GraphBLAS reference implementation is provided, but the use of GraphBLAS is not required in this Graph Challenge. As with prior Graph Challenges the goal is to provide a well-defined context for demonstrating innovation. Graph Challenge participants are free to select (with accompanying explanation) the Graph Challenge elements that are appropriate for highlighting their innovations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_08115 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Anonymized Network Sensing Graph Challenge Jananthan, Hayden Jones, Michael Arcand, William Bestor, David Bergeron, William Burrill, Daniel Buluc, Aydin Byun, Chansup Davis, Timothy Gadepally, Vijay Grant, Daniel Houle, Michael Hubbell, Matthew Luszczek, Piotr Michaleas, Peter Milechin, Lauren Milner, Chasen Morales, Guillermo Morris, Andrew Mullen, Julie Patel, Ritesh Pentland, Alex Pisharody, Sandeep Prout, Andrew Reuther, Albert Rosa, Antonio Wachman, Gabriel Yee, Charles Kepner, Jeremy Networking and Internet Architecture Discrete Mathematics Performance Software Engineering Combinatorics The MIT/IEEE/Amazon GraphChallenge encourages community approaches to developing new solutions for analyzing graphs and sparse data derived from social media, sensor feeds, and scientific data to discover relationships between events as they unfold in the field. The anonymized network sensing Graph Challenge seeks to enable large, open, community-based approaches to protecting networks. Many large-scale networking problems can only be solved with community access to very broad data sets with the highest regard for privacy and strong community buy-in. Such approaches often require community-based data sharing. In the broader networking community (commercial, federal, and academia) anonymized source-to-destination traffic matrices with standard data sharing agreements have emerged as a data product that can meet many of these requirements. This challenge provides an opportunity to highlight novel approaches for optimizing the construction and analysis of anonymized traffic matrices using over 100 billion network packets derived from the largest Internet telescope in the world (CAIDA). This challenge specifies the anonymization, construction, and analysis of these traffic matrices. A GraphBLAS reference implementation is provided, but the use of GraphBLAS is not required in this Graph Challenge. As with prior Graph Challenges the goal is to provide a well-defined context for demonstrating innovation. Graph Challenge participants are free to select (with accompanying explanation) the Graph Challenge elements that are appropriate for highlighting their innovations. |
| title | Anonymized Network Sensing Graph Challenge |
| topic | Networking and Internet Architecture Discrete Mathematics Performance Software Engineering Combinatorics |
| url | https://arxiv.org/abs/2409.08115 |