Anomaly Detection

Quick Reference: Logging & Monitoring

Quick Reference

Anomaly: Unusual pattern in metrics/logs

Detection Methods: Statistical thresholds, ML models, time-series analysis

Use Cases: Security threats, performance issues, system failures

Clear Definition

Anomaly Detection identifies unusual patterns in system metrics, logs, or behavior that may indicate problems, security threats, or performance issues.

šŸ’” Key Insight: Detect anomalies early to prevent issues. Use statistical methods and ML for accurate detection.

Core Concepts

Detection Methods

  1. Threshold-Based: Simple rules (CPU > 90%)
  2. Statistical: Z-scores, moving averages
  3. ML-Based: Isolation Forest, LSTM
  4. Time-Series: Detect patterns over time

Alerting

  • Alert Fatigue: Too many false positives
  • Alert Routing: Route to right team
  • Escalation: Escalate if not acknowledged

Best Practices

  1. Reduce False Positives: Tune thresholds
  2. Context: Include relevant information
  3. Actionable: Alerts should trigger action
  4. Review: Regularly review and tune

Quick Reference Summary

Anomaly Detection: Identify unusual patterns in system behavior.

Methods: Thresholds, statistical, ML-based.

Key: Reduce false positives, make alerts actionable.

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