Detection: Unusually Long Command Line - MLTK

Description

The following analytic identifies unusually long command lines executed on hosts, which may indicate malicious activity. It leverages the Machine Learning Toolkit (MLTK) to detect command lines with lengths that deviate from the norm for a given user. This is significant for a SOC as unusually long command lines can be a sign of obfuscation or complex malicious scripts. If confirmed malicious, this activity could allow attackers to execute sophisticated commands, potentially leading to unauthorized access, data exfiltration, or further compromise of the system.

Search

 1
 2| 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 
 3| `drop_dm_object_name(Processes)` 
 4| `security_content_ctime(firstTime)`
 5| `security_content_ctime(lastTime)`
 6| eval processlen=len(process) 
 7| search user!=unknown 
 8| apply cmdline_pdfmodel threshold=0.01 
 9| rename "IsOutlier(processlen)" as isOutlier 
10| search isOutlier > 0 
11| table firstTime lastTime user dest process_name process processlen count 
12| `unusually_long_command_line___mltk_filter`

Data Source

Macros Used

Annotations

Default Configuration

This detection is configured by default in Splunk Enterprise Security to run with the following settings:

Implementation

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 use long command lines for installs or updates. You should review identified command lines for legitimacy. You may modify the first part of the search to omit legitimate command lines from consideration. If you are seeing more results than desired, you may consider changing the value of threshold in the search to a smaller value. You should also periodically re-run the support search to re-build the ML model on the latest data. You may get unexpected results if the user identified in the results is not present in the data used to build the associated model.

Associated Analytic Story

• Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns

• Ransomware

• Suspicious Command-Line Executions

• Unusual Processes

Risk Based Analytics (RBA)

Detection Testing

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: GitHub | Version: 2