name: Potentially malicious code on commandline
id: 9c53c446-757e-11ec-871d-acde48001122
version: 1
date: '2022-01-14'
author: Michael Hart, Splunk
type: Anomaly
datamodel:
- Endpoint
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.
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: To successfully implement this search, you need to be ingesting
  logs with the process name, parent process, and command-line executions from your
  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
  Sysmon TA.  You will also need to install the Machine Learning Toolkit version 5.3
  or above to apply the pretrained model.
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
  confidence: 20
  context:
  - Source:Endpoint
  - Stage:Execution
  dataset:
  - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/malicious_cmd_line_samples/windows-sysmon.log
  impact: 60
  kill_chain_phases:
  - Exploitation
  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
  supported_tas:
  - Splunk_TA_microsoft_sysmon
  asset_type: Endpoint