arXiv Machine Learning July 21, 2026 By Thu T. H. Doan, Mohammad Saiful Islam, Andriy Miranskyy, Ngoc-Thanh Nguyen, Rogardt Heldal, Patrizio Pelliccione

ClouDens: Operational Context-Aware Anomaly Detection for Large-scale Cloud System Monitoring

Read the original on arXiv Machine Learning →

arXiv:2607. 18127v1 Announce Type: cross Abstract: With the rapid growth of cloud computing infrastructures in scale and complexity, network monitoring for Large-scale Cloud Systems (LCSs) has become increasingly challenging, requiring automated and reliable anomaly detection to maintain service availability.

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

arXiv AI

Jun 10

Anomaly Detection and Root Cause Analysis for Microservice Systems

arXiv:2606. 09942v1 Announce Type: cross Abstract: Microservice systems are widely used to build cloud applications, yet their complexity makes failures inevitable, degrading user experience and causing economic loss.

By Luan Pham

multimodal benchmarks

arXiv Machine Learning

Jun 5

Scalable Temporal Anomaly Causality Discovery in Large Systems: Achieving Computational Efficiency with Binary Anomaly Flag Data

arXiv:2412. 11800v4 Announce Type: replace Abstract: Extracting anomaly causality facilitates diagnostics once monitoring systems detect system faults.

By Mulugeta Weldezgina Asres, Christian Walter Omlin, The CMS-HCAL Collaboration

efficiency

arXiv Machine Learning

Jun 30

Towards Improved Anomaly Detection for Cloud Cybersecurity via Graph Neural Networks

arXiv:2606. 28923v1 Announce Type: new Abstract: Detecting security threats in an organization's cloud computing environment has become necessary due to the increased reliance on cloud infrastructure.

By Manu Nandan, TJ Jaymes, Michael Brautbar, Edward Raff

arXiv Machine Learning

Jun 11

NetBurst: Event-Centric Forecasting of Bursty, Intermittent Time Series

arXiv:2510. 22397v2 Announce Type: replace-cross Abstract: Network operators monitor their infrastructure by collecting telemetry data such as packet counts, byte rates, or flow volumes, yet answering the questions that effective operations demand -- forecasting future load, diagnosing and characterizing anomalies, and searching for and retrieving historical precedents -- requires more than raw measurements.

By Satyandra Guthula, Jaber Daneshamooz, Charles Fleming, Kesheng Wu, Walter Willinger, Arpit Gupta

benchmarks safety

arXiv Machine Learning

Aug 4

CAPMix: Robust KPI Anomaly Detection for AIOps in Noisy and Dynamic Environments

arXiv:2509. 06419v2 Announce Type: replace Abstract: Time-series anomaly detection is crucial in AIOps for maintaining large-scale service reliability.

By Xudong Mou, Rui Wang, Tiejun Wang, Zexin Wu, Fangda Guo, Jie Sun, Shiru Chen, Penghao Zhang, Tiezi Zhang, Tianyu Wo, Hao Peng, Chunming Hu, Xudong Liu, Renyu Yang

benchmarks

arXiv AI

Jul 22

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios

arXiv:2607. 18289v1 Announce Type: cross Abstract: Continual anomaly detection (CAD) studies how models can adapt to evolving data distributions while retaining performance on previously observed regimes.

By Kamil Faber, Mateusz Smendowski, Roberto Corizzo

benchmarks

Related stories

Anomaly Detection and Root Cause Analysis for Microservice Systems

Scalable Temporal Anomaly Causality Discovery in Large Systems: Achieving Computational Efficiency with Binary Anomaly Flag Data

Towards Improved Anomaly Detection for Cloud Cybersecurity via Graph Neural Networks

NetBurst: Event-Centric Forecasting of Bursty, Intermittent Time Series

CAPMix: Robust KPI Anomaly Detection for AIOps in Noisy and Dynamic Environments

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios