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
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.
Supported Add-on (TA)
List of Splunk Add-on’s tested to work with the analytic.
List of fields required to use this analytic.
How To Implement
To successfully implement this search, you need to be ingesting logs with the full process path in the process field of CIM's Process data model. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed sc.exe may be used.
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.
source | version: 2