Attacker Tools On Endpoint
This search looks for execution of commonly used attacker tools on an endpoint.
- Type: TTP
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2021-11-04
- Author: Bhavin Patel, Splunk
- ID: a51bfe1a-94f0-48cc-b4e4-16a110145893
Kill Chain Phase
- CIS 10
1 2 3 4 5 6 7 8 | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process) as process values(Processes.parent_process) as parent_process from datamodel=Endpoint.Processes where Processes.dest!=unknown Processes.user!=unknown by Processes.dest Processes.user Processes.process_name Processes.process | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | lookup attacker_tools attacker_tool_names AS process_name OUTPUT description | search description !=false | `attacker_tools_on_endpoint_filter`
The SPL above uses the following Macros:
attacker_tools_on_endpoint_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
The SPL above uses the following Lookups:
- attacker_tools with data
List of fields required to use this analytic.
How To Implement
To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes 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 usually generated via logs that report process tracking in your Windows audit settings.
Known False Positives
Some administrator activity can be potentially triggered, please add those users to the filter macro.
Associated Analytic Story
|64.0||80||80||An attacker tool $process_name$,listed in attacker_tools.csv is executed on host $dest$ by User $user$. This process $process_name$ is known to do- $description$|
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
Replay any dataset to Splunk Enterprise by using our
replay.py tool or the UI.
Alternatively you can replay a dataset into a Splunk Attack Range
source | version: 2