[Rule Tuning] Linux 3rd Party EDR Support - Crowdstrike and S1 - 1

[w0rk3r]

Issues

https://github.com/elastic/ia-trade-team/issues/503
https://github.com/elastic/ia-trade-team/issues/505

Summary

Introduces rule modifications to add support to SentinelOne and Crowdstrike where possible.

Crowdstrike events are pending integration adjustments (documented here), but they often lack enough context when comparing to S1 and Elastic Defend.

While these rules were not tested by generating alerts due to the lack of access to a CrowdStrike environment, the following steps were taken to ensure accuracy:

• Verified that the fields used are populated by the target EDR in the specified event category, which is documented here at the Linux EDR Field Compatibility Matrix
• Checked for and resolved any base field (e.g., event.type, event.action, host.os.type, etc.) incompatibilities.
• Manually tested the queries with small modifications in the field values to ensure the logic worked.

[protectionsmachine]

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]

Minstack was discussed in great detail, this looks good to me