What approaches are used for anomaly detection in logs and metrics?

Various approaches are used for anomaly detection in logs and metrics:

• Statistical methods: analyzing data distribution, identifying values outside the confidence interval (e.g., z-score).
• Time series models: using ARIMA, Holt-Winters for forecasting and detecting deviations from expected behavior.
• Machine learning: clustering algorithms (e.g., DBSCAN), density-based methods, Isolation Forests for identifying unusual patterns.
• Deep learning: autoencoders, recurrent neural networks (LSTM) for modeling normal behavior and detecting anomalies.

Logs often involve parsing and transforming data into structured formats, then applying models to identify unusual sequences or event frequencies. Metrics involve trend analysis, seasonality, and sharp jumps.

The choice of approach depends on data volume, detection speed requirements, and available resources.