mirror of
https://github.com/splunk/security_content
synced 2026-06-08 17:32:49 +00:00
P4T12ICK
81 lines
3.5 KiB
YAML
81 lines
3.5 KiB
YAML
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
|