arXiv AI By Francesco Spinelli, Esteban Municio, Pau Baguer, Gines Garcia-Aviles, Xavier Costa-Perez

DAST: A VLM-LLM Framework for Cross-Interface Anomaly Detection in O-RAN

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arXiv:2606. 06261v1 Announce Type: cross Abstract: O-RAN enables a disaggregated baseband stack with programmable functions that communicate over standardized open interfaces.

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

arXiv Machine Learning
Aug 7

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

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.

By Mohammad Arafath Uddin Shariff, Byrav Ramamurthy
arXiv Machine Learning
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CALIBURN: Operationally Calibrated Streaming Intrusion Detection with Regime-Dependent Conformal Risk Control

arXiv:2605. 24696v2 Announce Type: replace-cross Abstract: Streaming intrusion detection systems must process flows continuously under bounded memory, yet most leave alerting-threshold selection as a post-hoc tuning problem incompatible with production, where operators commit in advance to alert budgets, misclassification costs, and Service Level Objectives.

By Michel A. Youssef