[Rule Tuning] Tuning Azure Entra Sign-in Brute Force against Microsoft 365 Accounts

[terrancedejesus]

Pull Request

Issue link(s):

• [Rule Tuning] Azure Entra Sign-in Brute Force against Microsoft 365 Accounts #4262

Summary - What I changed

Tunes the Azure Entra Sign-in Brute Force against Microsoft 365 Accounts based on community feedback in linked issue and testing. Also adjusts lookback window for Potential Microsoft 365 User Account Brute Force to be accurate with temporal logic.

Additional info:

• Reduced time window from 30 to 5 minutes to improve detection granularity and responsiveness.
• Replaced basic count logic with behavior-based classification (bf_type) to detect password spraying, credential stuffing, and guessing.
• Added filtering on known brute-force-related result_type error codes (e.g., 50126, 50034, 50053) to reduce noise.
• Excluded benign or noisy user agents, such as "Mozilla/5.0 (compatible; MSAL 1.0) PKeyAuth/1.0".
• Included additional application and token context like client_app_display_name, incoming_token_type, and authentication_requirement.
• Expanded output with device, session, and geo fields for better triage (device_detail_device_id, session_id, source_orgs, etc.).
• Ensured user ID is present and valid by checking user_id IS NOT NULL AND user_id != "".
• Switched to COUNT_DISTINCT(source.ip) instead of total count to accurately track unique login sources.
• Added duration calculation (duration_seconds) and tracked the first/last seen timestamps.
• Grouped and enriched statistics by broader dimensions, improving alert value and context in Elastic's UI.

How To Test

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.

[Samirbous]

Nice!

[imays11]

This is great!