splunk-security_content/detections/endpoint/potentially_malicious_code_on_commandline.yml

name: Potentially malicious code on commandline
id: 9c53c446-757e-11ec-871d-acde48001122
version: 1
date: '2022-01-14'
author: Michael Hart, Splunk
status: production
type: Anomaly
description: 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.
data_source: []
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 hallmarks of malicious activity run
    by $user$ found on $dest$ with commandline $process$
  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