Iterative selection of categorical variables for log data anomaly detection

DOI10.1007/978-3-030-88418-5_36zbMath1496.68069OpenAlexW3201763010MaRDI QIDQ2146163

Max Landauer, Georg Höld, Markus Wurzenberger, Andreas Rauber, Florian Skopik

Publication date: 15 June 2022

Full work available at URL: https://doi.org/10.1007/978-3-030-88418-5_36

Mathematics Subject Classification ID

Computer security (68M25) Statistical aspects of big data and data science (62R07)

Uses Software

• GitHub
• Apache Spark

Cites Work

• Unnamed Item
• A bias-correction for Cramér's \(V\) and Tschuprow's \(T\)
• Large scale anomaly detection in mixed numerical and categorical input spaces

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