Forecasting and Anomaly Detection
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Next-Generation Firewall

Forecasting and Anomaly Detection

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Forecasting and Anomaly Detection

Learn how to generate alerts and incidents by forecasting and anomaly detection.
Where Can I Use This?
What Do I Need?
  • NGFW (Managed by Strata Cloud Manager)
  • NGFW (Managed by PAN-OS or Panorama)
  • VM-Series, funded with Software NGFW Credits
  • AIOps for NGFW Free (use the AIOps for NGFW Free app)
    or
  • AIOps for NGFW Premium license (use the Strata Cloud Manager app)
Normally,
AIOps for NGFW
detects issues by applying fixed rules to the metrics in your deployment. For example, if Management Plane CPU Usage exceeds 85%, the metric enters a Critical state.
However, to alert you to events that fixed rules might miss,
AIOps for NGFW
can use machine learning to understand your deployment and offer you additional alerts and incidents tailored to your usage trends.
  • Forecast-Based Alerts
    help you anticipate issues by projecting how a device metric may change and alerting you accordingly.
  • Anomaly-Based Alerts
    establish a baseline behavior for a device metric and alert you when that metric crosses the
    Anomaly Sensitivity Settings
    that you specify.
The benefits of forecasting and anomaly detection are as follows:
  • Proactive Management: By predicting potential issues and identifying anomalies early, administrators can take proactive measures to prevent problems, reducing downtime and improving overall network performance.
  • Enhanced Security: Detecting unusual patterns and behaviors can help identify security threats and vulnerabilities, allowing for timely intervention and mitigation.
  • Optimized Resources: Forecasting helps in better resource planning and allocation, ensuring that the network infrastructure is adequately prepared to handle future demands.
Navigate to
Incidents & Alerts
Incident & Alert Settings
Forecast and Anomaly Incidents & Alerts
.
AIOps for NGFW
generates alerts and incidents that dynamically adjust based on the metric’s historical value and your usage trends. Deviations from the normality band can indicate potential issues. You can adjust this setting to control the sensitivity level of the anomaly detection algorithm.

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