Detection: Email files written outside of the Outlook directory

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.

Description

The following analytic detects email files (.pst or .ost) being created outside the standard Outlook directories. It leverages the Endpoint.Filesystem data model to identify file creation events and filters for email files not located in "C:\Users*\My Documents\Outlook Files*" or "C:\Users*\AppData\Local\Microsoft\Outlook*". This activity is significant as it may indicate data exfiltration or unauthorized access to email data. If confirmed malicious, an attacker could potentially access sensitive email content, leading to data breaches or further exploitation within the network.

1
2| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name=*.pst OR Filesystem.file_name=*.ost) Filesystem.file_path != "C:\\Users\\*\\My Documents\\Outlook Files\\*"  Filesystem.file_path!="C:\\Users\\*\\AppData\\Local\\Microsoft\\Outlook*" by Filesystem.action Filesystem.process_id Filesystem.file_name Filesystem.dest 
3| `drop_dm_object_name("Filesystem")` 
4| `security_content_ctime(firstTime)` 
5| `security_content_ctime(lastTime)`
6| `email_files_written_outside_of_the_outlook_directory_filter`

Data Source

Name Platform Sourcetype Source
Sysmon EventID 11 Windows icon Windows 'xmlwineventlog' 'XmlWinEventLog:Microsoft-Windows-Sysmon/Operational'

Macros Used

Name Value
security_content_ctime convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$)
email_files_written_outside_of_the_outlook_directory_filter search *
email_files_written_outside_of_the_outlook_directory_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
T1114 Email Collection Collection
T1114.001 Local Email Collection Collection
KillChainPhase.EXPLOITAITON
NistCategory.DE_CM
Cis18Value.CIS_10
Ember Bear
Magic Hound
Scattered Spider
Silent Librarian
APT1
Chimera
Magic Hound
RedCurl
Winter Vivern

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 Notable Yes
Rule Title %name%
Rule Description %description%
Notable Event Fields user, dest
Creates Risk Event True
This configuration file applies to all detections of type TTP. These detections will use Risk Based Alerting and generate Notable Events.

Implementation

To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes.

Known False Positives

Administrators and users sometimes prefer backing up their email data by moving the email files into a different folder. These attempts will be detected by the search.

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: 5