splunk-security_content/detections/endpoint/potentially_malicious_code_on_commandline.yml

name: Potentially malicious code on commandline
id: 9c53c446-757e-11ec-871d-acde48001122
version: 2
date: '2024-05-12'
author: Michael Hart, Splunk
status: production
type: Anomaly
description: The following analytic detects potentially malicious command lines using
  a pretrained machine learning text classifier. It identifies unusual keyword combinations
  in command lines, such as "streamreader," "webclient," "mutex," "function," and
  "computehash," which are often associated with adversarial PowerShell code execution
  for C2 communication. This detection leverages data from Endpoint Detection and
  Response (EDR) agents, focusing on command lines longer than 200 characters. This
  activity is significant as it can indicate an attempt to execute malicious scripts,
  potentially leading to unauthorized code execution, data exfiltration, or further
  system compromise.
data_source:
- Sysmon EventID 1
search: '| 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`'
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.
references:
- https://attack.mitre.org/techniques/T1059/003/
- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1059.001/T1059.001.md
tags:
  analytic_story:
  - Suspicious Command-Line Executions
  asset_type: Endpoint
  confidence: 20
  impact: 60
  message: Unusual command-line execution with command line length greater than 200
    found on $dest$ with commandline value - [$process$]
  mitre_attack_id:
  - T1059.003
  observable:
  - name: dest
    type: Hostname
    role:
    - Victim
  - name: user
    type: User
    role:
    - Victim
  product:
  - Splunk Enterprise
  - Splunk Enterprise Security
  - Splunk Cloud
  required_fields:
  - _time
  - Processes.process
  - Processes.parent_process_name
  - Processes.process_name
  - Processes.parent_process
  - Processes.user
  - Processes.dest
  risk_score: 12
  security_domain: endpoint
tests:
- name: True Positive Test
  attack_data:
  - data: 
      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/malicious_cmd_line_samples/windows-sysmon.log
    source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
    sourcetype: xmlwineventlog