arXiv Machine Learning By Nurullah Aksu, Ali Fuat Sahin, Semiha Tedik Ba\c{s}aran

Explainability Boosted Anomaly Detection Framework for O-RAN based NextG Networks

Read the original on arXiv Machine Learning →

arXiv:2608. 14826v1 Announce Type: cross Abstract: The wireless networks have historically faced significant security vulnerabilities, necessitating advanced anomaly detection mechanisms, especially as networks evolve towards 6G and beyond.

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

arXiv AI
2d ago

Interactive Analysis of Global Explanations using Aggregated Class Activation Maps for Network Data

arXiv:2608. 13575v1 Announce Type: cross Abstract: Recent machine learning (ML) advances have demonstrated that deep learning (DL) achieves impressive results in different application domains, including the classification of computer network traffic to corresponding applications.

By Igor Cherepanov, David Sessler, Alex Ulmer, Felix Wagner, Throsten May, J\"orn Kohlhammer
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