THIS IS A EXPERIMENTAL DETECTION
This detection has been marked experimental by the Splunk Threat Research team. This means we have not been able to test, simulate, or build datasets for this detection. Use at your own risk. This analytic is NOT supported.
This search will return a table of processes in the a given window, remove process names which are in the allowed list and list out the top 30 rare processes discovered on different hosts.
- Type: Anomaly
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2022-11-10
- Author: Bhavin Patel, Splunk
- ID: 44fddcb2-8d3b-454c-874e-7c6de5a4f7ac
Kill Chain Phase
- CIS 10
1 2 3 4 5 6 7 8 9 10 | tstats `security_content_summariesonly` count values(Processes.dest) as dest values(Processes.user) as user min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.process_name | rename Processes.process_name as process | `filter_rare_process_allow_list` | sort count | head 30 | rex field=user "(?<user_domain>.*)\\\\(?<user_name>.*)" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_rare_executables_filter`
The SPL above uses the following Macros:
detect_rare_executables_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
The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the
Processes node of the
Endpoint data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
Known False Positives
Some legitimate processes may be only rarely executed in your environment. As these are identified, update
rare_process_allow_list_local.csv to filter them out of your search results.
Associated Analytic Story
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: 3