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Anomaly Detection

Anomaly Detection

Definition: Anomaly detection is the process of identifying patterns or events that deviate significantly from normal behavior. It involves monitoring data, identifying outliers, and investigating potential causes to understand and mitigate anomalies.

Examples and References:





Tools and Products for Anomaly Detection:

1. Splunk:

2. Datadog:

3. New Relic:

4. Sumo Logic:

5. Amazon CloudWatch:

Additional Resources:

Related Terms to Anomaly Detection:

1. Outlier Detection:

2. Novelty Detection:

3. Change Detection:

4. Event Detection:

5. Fault Detection:

6. Intrusion Detection:

7. Fraud Detection:

8. Root Cause Analysis:

9. Predictive Analytics:

10. Machine Learning for Anomaly Detection:


Prerequisites for Anomaly Detection:

1. Data Collection and Storage:

2. Data Preprocessing:

3. Definition of Normal Behavior:

4. Selection of Anomaly Detection Algorithm:

5. Training and Tuning the Algorithm:

6. Deployment and Monitoring:

7. Alerting and Notification:

8. Root Cause Analysis:

What’s next?

Next Steps After Anomaly Detection:

1. Investigation and Root Cause Analysis:

2. Prioritization and Remediation:

3. Continuous Monitoring and Adaptation:

4. Integration with Incident Management:

5. Performance Evaluation and Improvement:

6. Knowledge Sharing and Collaboration:

7. Proactive Anomaly Prevention:

8. Continuous Learning and Improvement: