This analytic is to detect a suspicious modification of time provider registry for persistence and autostart. This technique can allow the attacker to persist on the compromised host and autostart as soon as the machine boot up. This TTP can be a good indicator of suspicious behavior since this registry is not commonly modified by normal user or even an admin.
- Type: TTP
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2022-01-26
- Author: Teoderick Contreras, Splunk
- ID: 5ba382c4-2105-11ec-8d8f-acde48001122
Kill Chain Phase
1 2 3 4 5 6 7 8 9 10 11 | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path ="*\\CurrentControlSet\\Services\\W32Time\\TimeProviders*" by _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)` |rename process_guid as proc_guid |join proc_guid, _time [ | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes by _time span=1h Processes.process_id Processes.process_name Processes.process Processes.dest Processes.parent_process_name Processes.parent_process Processes.process_guid | `drop_dm_object_name(Processes)` |rename process_guid as proc_guid | fields _time dest user parent_process_name parent_process process_name process_path process proc_guid registry_path registry_value_name registry_value_data] | table _time dest user parent_process_name parent_process process_name process_path process proc_guid registry_path registry_value_name registry_value_data | `time_provider_persistence_registry_filter`
The SPL above uses the following Macros:
time_provider_persistence_registry_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
Associated Analytic Story
|80.0||80||100||modified/added/deleted registry entry $Registry.registry_path$ in $dest$|
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: 2