The search looks for reg.exe modifying registry keys that define Windows services and their configurations.
- Type: TTP
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2020-11-26
- Author: Rico Valdez, Splunk
- ID: 8470d755-0c13-45b3-bd63-387a373c10cf
Kill Chain Phase
- CIS 3
- CIS 5
- CIS 8
1 2 3 4 5 6 | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name values(Processes.user) as user FROM datamodel=Endpoint.Processes where Processes.process_name=reg.exe Processes.process=*reg* Processes.process=*add* Processes.process=*Services* by Processes.process_id Processes.dest Processes.process | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `reg_exe_manipulating_windows_services_registry_keys_filter`
The SPL above uses the following Macros:
reg_exe_manipulating_windows_services_registry_keys_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
List of fields required to use this analytic.
How To Implement
To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry.
Known False Positives
It is unusual for a service to be created or modified by directly manipulating the registry. However, there may be legitimate instances of this behavior. It is important to validate and investigate, as appropriate.
Associated Analytic Story
|45.0||75||60||A reg.exe process $process_name$ with commandline $process$ in host $dest$|
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
source | version: 5