Question: How does the use of machine learning contribute to effective anomaly detection in data store monitoring?
Answer:
Machine learning in data store monitoring dynamically adapts to changing patterns and identifies anomalies, improving the accuracy of anomaly detection.
MCQ: How does the use of machine learning contribute to effective anomaly detection in data store monitoring?
Explanation:
Machine learning in data store monitoring dynamically adapts to changing patterns and identifies anomalies, improving the accuracy of anomaly detection.
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Related Questions
- 1. What is the role of threat intelligence feeds in enhancing monitoring capabilities for data stores?
- 2. How can data anonymization contribute to privacy in log entries?
- 3. What is the purpose of continuous monitoring in web security?
- 4. How does the use of honeypots contribute to monitoring data stores?
- 5. Why is it important to monitor user access and privilege changes in data stores?
- 6. How does log rotation contribute to efficient log management in web security?
- 7. What is the purpose of penetration testing in the context of log entries and data store security?
- 8. How can user behavior analytics contribute to identifying anomalous activities in data store monitoring?
- 9. What is the significance of integrating logs with security information and event management (SIEM) systems?
- 10. Why is user education important for web security?
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