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Anomaly Detection Anomaly Settings

Configuring Anomaly Sensitivity

Fine-tune detection thresholds for your environment

Accessing Sensitivity Settings

Navigate to Settings → Monitoring → Anomaly Detection to configure how sensitive the detection system should be. Proper configuration helps balance catching real issues versus generating false positives.

Using Presets

For quick configuration, choose from our recommended presets based on your monitoring needs:

Sensitivity Presets
🔇
Quiet
Only critical issues. Best for stable environments.
⚖️
Balanced
Good mix of coverage and noise. Recommended.
🔔
Sensitive
Catch subtle issues. Good for critical systems.
⚙️
Custom
Fine-tune each setting individually.

Custom Configuration

Detection Thresholds
CPU Anomaly Threshold (σ) 2.0σ
1.0σ (Sensitive) 3.0σ (Quiet)
Memory Anomaly Threshold (σ) 1.8σ
1.0σ 3.0σ
Traffic Anomaly Threshold (σ) 2.5σ
1.0σ 3.0σ
Detection Settings
Time-of-Day Awareness
Adjust baselines based on time patterns (weekday vs weekend, business hours vs off-hours)
Seasonality Detection
Account for weekly and monthly patterns in your data
Correlated Event Suppression
Reduce duplicate anomalies from the same root cause
Maintenance Window Awareness
Suppress anomalies during scheduled maintenance windows

Understanding Standard Deviation (σ)

The sensitivity thresholds are measured in standard deviations (σ) from the learned baseline:

  • 1.0σ: ~32% of normal values fall outside this range (very sensitive)
  • 2.0σ: ~5% of normal values fall outside this range (balanced)
  • 3.0σ: ~0.3% of normal values fall outside this range (quiet)

Best Practices

  1. Start with Balanced: Use the balanced preset initially, then adjust based on your experience
  2. Give it Time: Wait at least 2 weeks before adjusting settings to let the baseline stabilize
  3. Use Feedback: Marking anomalies as "Expected" helps the system learn your patterns
  4. Consider Criticality: Use lower thresholds for business-critical systems
  5. Review Regularly: Check your anomaly detection effectiveness monthly

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