[Tuning] Elevation via SCM rules

[Samirbous]

• Service Creation via Local Kerberos Authentication (added winlog.event_data.ElevatedToken == "%%1843" to filter for un-elevated token).
• Windows Service Installed via an Unusual Client (added startswith~(user.domain, winlog.computer_name) and winlog.event_data.ServiceAccount == "LocalSystem" to limit to elevation as system and logon with local creds)

[github-actions]

Rule: Tuning - Guidelines

These guidelines serve as a reminder set of considerations when tuning an existing rule.

Documentation and Context

• Detailed description of the suggested changes.
• Provide example JSON data or screenshots.
• Provide evidence of reducing benign events mistakenly identified as threats (False Positives).
• Provide evidence of enhancing detection of true threats that were previously missed (False Negatives).
• Provide evidence of optimizing resource consumption and execution time of detection rules (Performance).
• Provide evidence of specific environment factors influencing customized rule tuning (Contextual Tuning).
• Provide evidence of improvements made by modifying sensitivity by changing alert triggering thresholds (Threshold Adjustments).
• Provide evidence of refining rules to better detect deviations from typical behavior (Behavioral Tuning).
• Provide evidence of improvements of adjusting rules based on time-based patterns (Temporal Tuning).
• Provide reasoning of adjusting priority or severity levels of alerts (Severity Tuning).
• Provide evidence of improving quality integrity of our data used by detection rules (Data Quality).
• Ensure the tuning includes necessary updates to the release documentation and versioning.

Rule Metadata Checks

• updated_date matches the date of tuning PR merged.
• min_stack_version should support the widest stack versions.
• name and description should be descriptive and not include typos.
• query should be inclusive, not overly exclusive. Review to ensure the original intent of the rule is maintained.

Testing and Validation

• Validate that the tuned rule's performance is satisfactory and does not negatively impact the stack.
• Ensure that the tuned rule has a low false positive rate.

[tradebot-elastic]

⛔️ Test failed

Results

• ❌ Service Creation via Local Kerberos Authentication (eql)
  • coverage_issue: no_rta
  • stack_validation_failed: no_rta

[tradebot-elastic]

⛔️ Test failed

Results

• ❌ Windows Service Installed via an Unusual Client (eql)
  • coverage_issue: no_rta
  • stack_validation_failed: no_rta
• ❌ Service Creation via Local Kerberos Authentication (eql)
  • coverage_issue: no_rta
  • stack_validation_failed: no_rta

[tradebot-elastic]

⛔️ Test failed

Results

• ❌ Windows Service Installed via an Unusual Client (eql)
  • coverage_issue: no_rta
  • stack_validation_failed: no_rta
• ❌ Service Creation via Local Kerberos Authentication (eql)
  • coverage_issue: no_rta
  • stack_validation_failed: no_rta

[tradebot-elastic]

⛔️ Test failed

Results

• ❌ Windows Service Installed via an Unusual Client (eql)
  • coverage_issue: no_rta
  • stack_validation_failed: no_rta
• ❌ Service Creation via Local Kerberos Authentication (eql)
  • coverage_issue: no_rta
  • stack_validation_failed: no_rta

[tradebot-elastic]

⛔️ Test failed

Results

• ❌ Windows Service Installed via an Unusual Client (eql)
  • coverage_issue: no_rta
  • stack_validation_failed: no_rta
• ❌ Service Creation via Local Kerberos Authentication (eql)
  • coverage_issue: no_rta
  • stack_validation_failed: no_rta