#### Search
```
| from datamodel Alerts.Alerts
| search app=osquery:results (name=pack_osx-attacks_OSX_ColdRoot_RAT_Launchd OR name=pack_osx-attacks_OSX_ColdRoot_RAT_Files)
| rename columns.path as path
| bucket _time span=30s
| stats count(path) by _time, host, user, path
| `osquery_pack___coldroot_detection_filter`
```
#### Macros
The SPL above uses the following Macros:
> :information_source:
> **osquery_pack_-_coldroot_detection_filter** is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
#### Required field
* _time
#### How To Implement
In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model
#### Known False Positives
There are no known false positives.
#### Associated Analytic story
* [ColdRoot MacOS RAT](/stories/coldroot_macos_rat)
#### RBA
| Risk Score | Impact | Confidence | Message |
| ----------- | ----------- |--------------|--------------|
| 25.0 | 50 | 50 | tbd |
> :information_source:
> The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
#### Reference
#### Test Dataset
Replay any dataset to Splunk Enterprise by using our [replay.py](https://github.com/splunk/attack_data#using-replaypy) tool or the [UI](https://github.com/splunk/attack_data#using-ui).
Alternatively you can replay a dataset into a [Splunk Attack Range](https://github.com/splunk/attack_range#replay-dumps-into-attack-range-splunk-server)
[*source*](https://github.com/splunk/security_content/tree/develop/detections/deprecated/osquery_pack___coldroot_detection.yml) \| *version*: **1**