[Rule Tuning] Potential Microsoft 365 User Account Brute Force

[terrancedejesus]

Pull Request

Issue link(s):

• [Rule Tuning] Potential Password Spraying of Microsoft 365 User Accounts #3934

Summary - What I changed

Adjusts the Potential Microsoft 365 User Account Brute Force rule with the following changes.

• Renames the rule
• Adjusts tags to include audit logs reference
• Adjusts o365.audit.Target.Type to ignore some system accounts that cause FPs
• Expands request_type to include oauth workflows as well. Note the BF testing we did was via OWA but also ROPC with Oauth which is valid for testing credentials.
• Changes the investigation guide to be more specific to the generated fields from ESQL
• For ESQL, adds VALUES() to retain important indicators for investigation
• Updated references (reference that no longer exists), expanded reference to password spraying investigation suggestions by MSFT
• Uses stats and case to identify if the brute force was password spraying, credential stuffing or generic

How To Test

• Please use the query in TRADE's serverless stack to see hits or validate syntax

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.

[shashank-elastic]

Rule ID is same, files have been renamed.

MITRE Technique to be corrected