Detection: Spike in File Writes

EXPERIMENTAL DETECTION

This detection status is set to experimental. The Splunk Threat Research team has not yet fully tested, simulated, or built comprehensive datasets for this detection. As such, this analytic is not officially supported. If you have any questions or concerns, please reach out to us at research@splunk.com.

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

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.

Search

1
2| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.action=created by _time span=1h, Filesystem.dest 
3| `drop_dm_object_name(Filesystem)` 
4| eventstats max(_time) as maxtime 
5| 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" 
6| eval upperBound=(avg+stdev*4), isOutlier=if((count > upperBound) AND num_data_samples >=20, 1, 0) 
7| search isOutlier=1 
8| `spike_in_file_writes_filter`

Data Source

Name	Platform	Sourcetype	Source	Supported App
Sysmon EventID 11	Windows	`'xmlwineventlog'`	`'XmlWinEventLog:Microsoft-Windows-Sysmon/Operational'`	N/A

Macros Used

Name	Value
security_content_summariesonly	`summariesonly=`summariesonly_config `allow_old_summaries=`oldsummaries_config `fillnull_value=`fillnull_config``
spike_in_file_writes_filter	`search *`

spike_in_file_writes_filter is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Annotations

- MITRE ATT&CK

+ Kill Chain Phases

+ NIST

+ CIS

- Threat Actors

ID	Technique	Tactic

NistCategory.DE_AE

Cis18Value.CIS_10

Default Configuration

This detection is configured by default in Splunk Enterprise Security to run with the following settings:

Setting	Value
Disabled	true
Cron Schedule	`0 * * * *`
Earliest Time	`-70m@m`
Latest Time	`-10m@m`
Schedule Window	`auto`
Creates Risk Event	True

This configuration file applies to all detections of type anomaly. These detections will use Risk Based Alerting.

Implementation

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

Risk Based Analytics (RBA)

Risk Message	Risk Score	Impact	Confidence
tbd	25	50	50

The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

Detection Testing

Test Type	Status	Dataset	Source	Sourcetype
Validation	Not Applicable	N/A	N/A	N/A
Unit	❌ Failing	N/A	`N/A`	`N/A`
Integration	❌ Failing	N/A	`N/A`	`N/A`

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: GitHub | Version: 4