arXiv Machine Learning August 7, 2026 By Mohammad Arafath Uddin Shariff, Byrav Ramamurthy

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining

Read the original on arXiv Machine Learning →

arXiv:2608. 05605v1 Announce Type: cross Abstract: Research and Education Networks (RENs) serve as critical infrastructure for scientific discovery, yet they face a unique security paradox: their normal traffic patterns which are characterized by massive, bursty "elephant flows" are statistically indistinguishable from volumetric attacks such as DDoS to conventional monitoring systems.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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