Accessing the Anomalies Dashboard
To view detected anomalies, navigate to Monitoring → Anomalies in the main menu. This page displays all anomalies detected by the machine learning system, sorted by severity and recency.
Detected Anomalies
Critical
Resource Anomaly
CPU Usage Spike on bbb-server-03
CPU usage jumped to 94% which is 3.2 standard deviations above the expected value for this time of day.
+312% deviation
High
Traffic Anomaly
Unusual Meeting Volume Pattern
Meeting creation rate is 2.1x higher than typical for Tuesday 3pm. Verify if this is expected (e.g., company event).
+210% deviation
Medium
Performance Anomaly
Increased API Response Latency
Average API response time increased to 450ms from the baseline of 180ms.
Pro Tip
Mark anomalies as "Expected" when they're caused by known events (like company meetings or maintenance). This helps the ML model learn your organization's patterns better.
Understanding Severity Levels
| Severity | Deviation | Description |
|---|---|---|
| ● Critical | > 3σ | Extreme deviation requiring immediate attention |
| ● High | 2-3σ | Significant deviation, should investigate soon |
| ● Medium | 1.5-2σ | Moderate deviation, monitor closely |
| ● Low | 1-1.5σ | Minor deviation, informational |
Filtering and Searching
- Time Range: Filter by last hour, 24 hours, 7 days, or custom range
- Severity: Show only specific severity levels
- Server: Filter anomalies by specific server
- Type: Filter by anomaly type (Resource, Traffic, Performance, etc.)
- Status: Show active, resolved, or marked-as-expected anomalies
Taking Action
For each anomaly, you can:
- Investigate: View detailed charts and correlated events
- Mark Expected: Tell the system this pattern is normal
- Create Alert Rule: Convert to a threshold-based alert for future monitoring
- Run Playbook: Execute an automated remediation workflow