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Description

The following analytics identifies a big number of instance of ransomware notes (filetype e.g .txt, .html, .hta) file creation to the infected machine. This behavior is a good sensor if the ransomware note filename is quite new for security industry or the ransomware note filename is not in your ransomware lookup table list for monitoring.

  • Type: Anomaly

  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud

  • Last Updated: 2021-03-12
  • Author: Teoderick Contreras
  • ID: eff7919a-8330-11eb-83f8-acde48001122

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1486 Data Encrypted for Impact Impact
Kill Chain Phase
  • Actions On Objectives
NIST
  • DE.AE
CIS20
  • CIS 10
CVE 


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`sysmon` EventCode=11 file_name IN ("*\.txt","*\.html","*\.hta") 
|bin _time span=10s 
| stats min(_time) as firstTime max(_time) as lastTime dc(TargetFilename) as unique_readme_path_count values(TargetFilename) as list_of_readme_path by Computer Image file_name 
| rename Computer as dest 
| where unique_readme_path_count >= 15 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `ransomware_notes_bulk_creation_filter`

Macros

The SPL above uses the following Macros:

:information_source: ransomware_notes_bulk_creation_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.

  • EventCode
  • file_name
  • _time
  • TargetFilename
  • dest
  • Image
  • user

How To Implement

You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.

Known False Positives

unknown

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
81.0 90 90 A high frequency file creation of $file_name$ in different file path in host $dest$

: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.

Reference

Test Dataset

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

source | version: 1