---
title: "Spike in File Writes"
excerpt: ""
categories:
  - Endpoint
last_modified_at: 2020-03-16
toc: true
toc_label: ""
tags:
  - Splunk Enterprise
  - Splunk Enterprise Security
  - Splunk Cloud
---

### ⚠️ WARNING THIS IS A EXPERIMENTAL DETECTION
We have not been able to test, simulate, or build datasets for this detection. Use at your own risk. This analytic is **NOT** supported.


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#### Description

The search looks for a sharp increase in the number of files written to a particular host

- **Type**: [Anomaly](https://github.com/splunk/security_content/wiki/Detection-Analytic-Types)
- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **Last Updated**: 2020-03-16
- **Author**: David Dorsey, Splunk
- **ID**: fdb0f805-74e4-4539-8c00-618927333aae

#### Search

```

| 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:
* [security_content_summariesonly](https://github.com/splunk/security_content/blob/develop/macros/security_content_summariesonly.yml)

Note that `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 field
* _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
* [SamSam Ransomware](/stories/samsam_ransomware)
* [Ryuk Ransomware](/stories/ryuk_ransomware)
* [Ransomware](/stories/ransomware)


#### Kill Chain Phase
* Actions on Objectives




Note that risk score is calculated base on the following formula: `(Impact * Confidence)/100`



#### Reference


#### Test Dataset
Replay any dataset to Splunk Enterprise by using our [`replay.py`](https://github.com/splunk/attack_data#using-replaypy) tool or the [UI](https://github.com/splunk/attack_data#using-ui).
Alternatively you can replay a dataset into a [Splunk Attack Range](https://github.com/splunk/attack_range#replay-dumps-into-attack-range-splunk-server)




[*source*](https://github.com/splunk/security_content/tree/develop/detections/experimental/endpoint/spike_in_file_writes.yml) \| *version*: **3**