What is Normal? A Big Data Observational Science Model of Anonymized Internet Traffic
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arXiv
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| Format: | Preprint |
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2024
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| author | Kepner, Jeremy 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 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 Michaleas, Peter |
| author_facet | Kepner, Jeremy 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 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 Michaleas, Peter |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_03111 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | What is Normal? A Big Data Observational Science Model of Anonymized Internet Traffic Kepner, Jeremy 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 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 Michaleas, Peter Networking and Internet Architecture Cryptography and Security Computers and Society Social and Information Networks 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. |
| title | What is Normal? A Big Data Observational Science Model of Anonymized Internet Traffic |
| topic | Networking and Internet Architecture Cryptography and Security Computers and Society Social and Information Networks |
| url | https://arxiv.org/abs/2409.03111 |