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The following analytic uses a pretrained machine learning text classifier to detect potentially malicious commandlines. The model identifies unusual combinations of keywords found in samples of commandlines where adversaries executed powershell code, primarily for C2 communication. For example, adversaries will leverage IO capabilities such as "streamreader" and "webclient", threading capabilties such as "mutex" locks, programmatic constructs like "function" and "catch", and cryptographic operations like "computehash". Although observing one of these keywords in a commandline script is possible, combinations of keywords observed in attack data are not typically found in normal usage of the commandline. The model will output a score where all values above zero are suspicious, anything greater than one particularly so.

  • Type: Anomaly
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Endpoint
  • Last Updated: 2022-01-14
  • Author: Michael Hart, Splunk
  • ID: 9c53c446-757e-11ec-871d-acde48001122




ID Technique Tactic
T1059.003 Windows Command Shell Execution
Kill Chain Phase
  • Installation
  • DE.AE
  • CIS 10
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel="Endpoint.Processes" by Processes.parent_process_name Processes.process_name Processes.process Processes.user Processes.dest  
| `drop_dm_object_name(Processes)`  
| where len(process) > 200 
| `potentially_malicious_code_on_cmdline_tokenize_score` 
| apply unusual_commandline_detection 
| eval score='predicted(unusual_cmdline_logits)', process=orig_process 
| fields - unusual_cmdline* predicted(unusual_cmdline_logits) orig_process 
| where score > 0.5 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `potentially_malicious_code_on_commandline_filter`


The SPL above uses the following Macros:

:information_source: potentially_malicious_code_on_commandline_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.process
  • Processes.parent_process_name
  • Processes.process_name
  • Processes.parent_process
  • Processes.user
  • Processes.dest

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

This model is an anomaly detector that identifies usage of APIs and scripting constructs that are correllated with malicious activity. These APIs and scripting constructs are part of the programming langauge and advanced scripts may generate false positives.

Associated Analytic Story


Risk Score Impact Confidence Message
12.0 60 20 Unusual command-line execution with command line length greater than 200 found on $dest$ with commandline value - [$process$]

: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.


Test Dataset

Replay any dataset to Splunk Enterprise by using our tool or the UI. Alternatively you can replay a dataset into a Splunk Attack Range

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