CTAL is an open-source lab that provides a privacy-safe foundation for explainable cloud threat detection by generating deterministic synthetic data and benchmarking three transparent detectors (rule-based, statistical, and optional Isolation Forest).
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NOASSERTION
2026-07-18
It intentionally avoids real, employer, or production data, offers reproducible benchmarking with a clear detection/evaluation boundary, and provides three complementary explainable detectors that do not use machine learning by default, making it a unique research and teaching tool.
Open-source cloud security anomaly detection lab with explainable rules, robust statistics, Isolation Forest, synthetic datasets, and a local dashboard.
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