WARNING THIS IS A EXPERIMENTAL analytic
We have not been able to test, simulate, or build datasets for this object. Use at your own risk. This analytic is NOT supported.
Command lines that are extremely long may be indicative of malicious activity on your hosts.
- Type: Anomaly
Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Last Updated: 2020-12-08
- Author: David Dorsey, Splunk
- ID: c77162d3-f93c-45cc-80c8-22f6a4264e7f
Kill Chain Phase
- Actions on Objectives
- CIS 8
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.Processes by Processes.user Processes.dest Processes.process_name Processes.process | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval processlen=len(process) | eventstats stdev(processlen) as stdev, avg(processlen) as avg by dest | stats max(processlen) as maxlen, values(stdev) as stdevperhost, values(avg) as avgperhost by dest, user, process_name, process | `unusually_long_command_line_filter` |eval threshold = 3 | where maxlen > ((threshold*stdevperhost) + avgperhost)
The SPL above uses the following Macros:
unusually_long_command_line_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
How To Implement
You must be ingesting endpoint data that tracks process activity, including parent-child relationships, from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the process field in the Endpoint data model.
Known False Positives
Some legitimate applications start with long command lines.
Associated Analytic story
- Suspicious Command-Line Executions
- Unusual Processes
- Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns
|42.0||70||60||Unusually long command line $Processes.process_name$ 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: 5