This analytic will identify suspicious series of process executions. We have observed that post exploit framework tools like Koadic and Meterpreter will launch an excessive number of processes with distinct file paths from Windows\Temp to execute actions on objective. This behavior is extremely anomalous compared to typical application behaviors that use Windows\Temp.
- Type: Anomaly
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2022-02-28
- Author: Michael Hart, Mauricio Velazco, Splunk
- ID: 23587b6a-c479-11eb-b671-acde48001122
Kill Chain Phase
- CIS 10
1 2 3 4 5 6 7 | tstats `security_content_summariesonly` values(Processes.process) as process distinct_count(Processes.process) as distinct_process_count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_path = "*\\Windows\\Temp\\*" by Processes.dest Processes.user _time span=20m | where distinct_process_count > 37 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_distinct_processes_from_windows_temp_filter`
The SPL above uses the following Macros:
excessive_distinct_processes_from_windows_temp_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
Many benign applications will create processes from executables in Windows\Temp, although unlikely to exceed the given threshold. Filter as needed.
Associated Analytic Story
|80.0||80||100||Multiple processes were executed out of windows\temp within a short amount of time on $dest$.|
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
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