Deciphering SIEM query performance: slow and noisy detections

// Understanding the Query Plan

SIEMs like Splunk, ELK, or QRadar can produce a wealth of data. However, slow query performance and noisy alerts often plague users. Understanding how to read a SIEM query plan can illuminate where improvements can be made.

When running a query, it’s critical to analyze the execution plan. Take Splunk as an example. Suppose you run the following search:

index=security_logs source="/var/log/auth.log" | stats count by user

To view the query performance, you can use the | explain command:

index=security_logs source="/var/log/auth.log" | explain | stats count by user

This will output details about how Splunk processes the query, including:

• Search phases: Each step in the query execution.
• Execution time: How long each phase takes.
• Data volume: Amount of data processed at each stage.

// Common Performance Bottlenecks

1. Unoptimized Field Extraction: If the SIEM has to extract fields on the fly during each query, performance suffers. For instance, if user is not indexed, every search for it will slow down.

2. Excessive Data Scanned: Queries that do not use time range filters or overly broad searches can lead to scanning large data volumes.

3. Redundant Operations: Chaining multiple commands that do not filter or reduce the dataset effectively can lead to unnecessary overhead.

Example of a Noisy Query

Consider a poorly constructed query:

index=security_logs | stats count by user | where count > 10

This query scans all records first before filtering, leading to high latency. Instead, refine it:

index=security_logs earliest=-24h | stats count by user | where count > 10

This adjusts the time scope, significantly reducing the dataset and improving response time.

// Best Practices for Query Optimization

• Use Indexes: Ensure your key fields are indexed, so the SIEM doesn’t have to perform field extraction on every query.
• Limit Time Ranges: Always apply time constraints to your queries to reduce the amount of data processed.
• Filter Early: Apply filtering as soon as possible in the query to minimize dataset size for subsequent operations.
• Review Query Plans Regularly: Get in the habit of reviewing query plans for any changes in performance as data grows.

// A Real-World Scenario

Let’s say you’ve been tasked with monitoring administrative access. An initial query may look like this:

index=security_logs sourcetype=access_logs | stats count by user

This query might generate excessive alerts, creating noise. Instead, using filters judiciously will sharpen your results:

index=security_logs sourcetype=access_logs earliest=-7d | stats count as access_count by user | where access_count > 5

The focused query reduces noise while still maintaining visibility into significant access patterns.

// Common Mistakes to Avoid

• Neglecting Time Constraints: Always remember to filter by time; without it, you might end up processing unnecessary data.
• Ignoring Indexing: Not indexing key fields can lead to increased query times and inefficiencies.
• Not Using the Explain Command: Failing to analyze query performance can lead to missed opportunities for optimization.

// Checklist for SIEM Query Optimization

1. Identify key fields for indexing. 2. Apply time constraints to queries. 3. Review execution plans regularly. 4. Minimize the scanned data volume. 5. Use filters early in the query process.

// Conclusion

Optimizing SIEM queries can drastically improve detection speed and reduce noise. Regularly analyzing query performance and making iterative improvements will help maintain a robust monitoring posture. Always conduct your experiments in a disposable lab environment to understand the behavior without impacting production systems. The curriculum at DaemonCore Academy is free, providing a range of resources to enhance your skills in these areas.