:warning: THIS IS A EXPERIMENTAL DETECTION

This detection has been marked experimental by the Splunk Threat Research team. This means we have not been able to test, simulate, or build datasets for this detection. Use at your own risk. This analytic is NOT supported.

Try in Splunk Security Cloud

Description

The following analytic detects a sharp increase in the number of files written to a specific host. It leverages the Endpoint.Filesystem data model, focusing on 'created' actions and comparing current file write counts against historical averages and standard deviations. This activity is significant as a sudden spike in file writes can indicate malicious activities such as ransomware encryption or data exfiltration. If confirmed malicious, this behavior could lead to significant data loss, system compromise, or further propagation of malware within the network.

  • Type: Anomaly
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Endpoint
  • Last Updated: 2024-05-16
  • Author: David Dorsey, Splunk
  • ID: fdb0f805-74e4-4539-8c00-618927333aae

Annotations

ATT&CK
Kill Chain Phase
NIST
  • DE.AE
CIS20
  • CIS 10
CVE 


1
2
3
4
5
6
7
8



| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.action=created by _time span=1h, Filesystem.dest 
| `drop_dm_object_name(Filesystem)` 
| eventstats max(_time) as maxtime 
| stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-1d@d"), count, null))) as "count" avg(eval(if(_time<relative_time(maxtime, "-1d@d"), count,null))) as avg stdev(eval(if(_time<relative_time(maxtime, "-1d@d"), count, null))) as stdev by "dest" 
| eval upperBound=(avg+stdev*4), isOutlier=if((count > upperBound) AND num_data_samples >=20, 1, 0) 
| search isOutlier=1 
| `spike_in_file_writes_filter`

Macros

The SPL above uses the following Macros:

:information_source: spike_in_file_writes_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.

  • _time
  • Filesystem.action
  • Filesystem.dest

How To Implement

In order to implement this search, you must populate the Endpoint file-system data model node. This is typically populated via endpoint detection and response product, such as Carbon Black or endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the file system.

Known False Positives

It is important to understand that if you happen to install any new applications on your hosts or are copying a large number of files, you can expect to see a large increase of file modifications.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
25.0 50 50 tbd

: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: 4