:warning: 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.

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Description

The following analytic detects unusually long command lines, which may indicate malicious activity. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on the length of command lines executed on hosts. This behavior is significant because attackers often use obfuscated or complex command lines to evade detection and execute malicious payloads. If confirmed malicious, this activity could lead to data theft, ransomware deployment, or further system compromise. Analysts should investigate the source and content of the command line, inspect relevant artifacts, and review concurrent processes to identify potential threats.

  • Type: Anomaly
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Endpoint
  • Last Updated: 2024-05-20
  • Author: David Dorsey, Splunk
  • ID: c77162d3-f93c-45cc-80c8-22f6a4264e7f

Annotations

ATT&CK
Kill Chain Phase
NIST
  • DE.AE
CIS20
  • CIS 10
CVE 


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| 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)

Macros

The SPL above uses the following Macros:

:information_source: 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.

Required fields

List of fields required to use this analytic.

  • _time
  • Processes.user
  • Processes.dest
  • Processes.process_name
  • Processes.process

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 applications start with long command lines.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
42.0 70 60 Unusually long command line $process_name$ on $dest$

:information_source: The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

Reference

Test Dataset

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: 6