什么是正常？匿名互联网流量的大数据观察科学模型

arXiv - CS - Social and Information Networks Pub Date : 2024-09-04 DOI:arxiv-2409.03111

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

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引用次数: 0

摘要

了解什么是正常行为是保护域的一个关键方面。其他域在观测科学方面投入了大量资金，以开发正常行为模型，从而更好地检测异常情况。高性能图形库（如 GraphBLAS）的最新进展与超级计算机相结合，可以处理所需的数万亿观测数据。我们利用这种方法合成了匿名互联网流量的低参数观测模型，并高度关注隐私问题。

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What is Normal? A Big Data Observational Science Model of Anonymized Internet Traffic

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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arXiv - CS - Social and Information Networks

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