Datasets:
Enhance dataset card: Add paper, project page links, task categories, and tags
#1
by
nielsr
HF Staff
- opened
README.md
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license: cc-by-4.0
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---
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# CloudAnoBench
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CloudAnoBench is a large-scale benchmark for context-aware anomaly detection in cloud environments, jointly incorporating both metrics and logs to more faithfully reflect real-world conditions. It consists of 1,252 labeled cases spanning 28 anomalous scenarios and 16 deceptive normal scenarios (approximately 200K lines), with explicit annotations for anomaly type and scenario. By including deceptive normal cases where anomalous-looking metric patterns are explained by benign log events, the benchmark introduces higher ambiguity and difficulty compared to prior datasets, thereby providing a rigorous testbed for evaluating both anomaly detection and scenario identification.
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## 📁 Dataset Structure
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Where:
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- `scenario_id`: Scenario number
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- `case_number`: Case number within the scenario
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- Extension: `.csv` for structured data, `.log` for raw logs
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---
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license: cc-by-4.0
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task_categories:
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- TIME_SERIES_FORECASTING
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- TEXT_CLASSIFICATION
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language:
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- en
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tags:
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- anomaly-detection
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- cloud-environments
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- metrics
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- logs
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- time-series
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---
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# CloudAnoBench
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Paper: [Towards Generalizable Context-aware Anomaly Detection: A Large-scale Benchmark in Cloud Environments](https://huggingface.co/papers/2508.01844)
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Project Page: https://jayzou3773.github.io/cloudanobench-agent/
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CloudAnoBench is a large-scale benchmark for context-aware anomaly detection in cloud environments, jointly incorporating both metrics and logs to more faithfully reflect real-world conditions. It consists of 1,252 labeled cases spanning 28 anomalous scenarios and 16 deceptive normal scenarios (approximately 200K lines), with explicit annotations for anomaly type and scenario. By including deceptive normal cases where anomalous-looking metric patterns are explained by benign log events, the benchmark introduces higher ambiguity and difficulty compared to prior datasets, thereby providing a rigorous testbed for evaluating both anomaly detection and scenario identification.
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## 📁 Dataset Structure
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Where:
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- `scenario_id`: Scenario number
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- `case_number`: Case number within the scenario
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- Extension: `.csv` for structured data, `.log` for raw logs
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