[Rule Tuning] Expand Scope of Entra ID Brute Force Sign-In Attempts

[terrancedejesus]

Pull Request

Issue link(s):

• https://github.com/elastic/ia-trade-team/issues/642

Summary - What I changed

Adjusts the Azure Entra ID Password Spraying (Non-Interactive SFA) rule to expand scope and limit error codes while adding additional columns for finalized dataset to help with post-investigation. Please see development linked issue (not meta) for more details.

How To Test

• Query can be used in TRADE stack

Checklist

• Added a label for the type of pr: bug, enhancement, schema, maintenance, Rule: New, Rule: Deprecation, Rule: Tuning, Hunt: New, or Hunt: Tuning so guidelines can be generated
• Added the meta:rapid-merge label if planning to merge within 24 hours
• Secret and sensitive material has been managed correctly
• Automated testing was updated or added to match the most common scenarios
• Documentation and comments were added for features that require explanation

Contributor checklist

• Have you signed the contributor license agreement?
• Have you followed the contributor guidelines?

[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.

[Aegrah]

Very cool rule/query @terrancedejesus !

[Aegrah]

Are these EVAL's costly in terms of performance, or not at all?

[terrancedejesus]

That is a great question. I assume it does increase performance as it is another task to execute in the pipeline, how performant - I am not sure.

[Aegrah]

This excludes any other bf_type's from the results?

[terrancedejesus]

Yes, it is meant to exclude all else. Since we are only aggregating by a time window and relying on CASE() to determine what type of BF - if we include other it would include signals for a bucket of failed logins which is just noise.

[Aegrah]

Not sure if we want to do any sorting/limiting at the end.

[terrancedejesus]

For sorting, I don't see any value added to sorting by failed login count, unique user count or alphabetically that would enable better action or prioritization. Limiting wise, I do not see any reason to limit the results since it is already aggregation-based unless we expect this to be noisy. Do you have any suggestions on limiting and why?