Detection: Monitor Email For Brand Abuse

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: 2024-04-16 ID: b2ea1f38-3a3e-4b8a-9cf1-82760d86a6b8 Author: David Dorsey, Splunk Type: TTP Product: Splunk Enterprise Security

Description

The following analytic identifies emails claiming to be sent from a domain similar to one you are monitoring for potential abuse. It leverages email header data, specifically the sender's address, and cross-references it with a lookup table of known domain permutations generated by the "ESCU - DNSTwist Domain Names" search. This activity is significant as it can indicate phishing attempts or brand impersonation, which are common tactics used in social engineering attacks. If confirmed malicious, this could lead to unauthorized access, data theft, or reputational damage.

Search

 1
 2| tstats `security_content_summariesonly` values(All_Email.recipient) as recipients, min(_time) as firstTime, max(_time) as lastTime from datamodel=Email by All_Email.src_user, All_Email.message_id 
 3| `drop_dm_object_name("All_Email")` 
 4| `security_content_ctime(firstTime)` 
 5| `security_content_ctime(lastTime)` 
 6| eval temp=split(src_user, "@") 
 7| eval email_domain=mvindex(temp, 1) 
 8| lookup update=true brandMonitoring_lookup domain as email_domain OUTPUT domain_abuse 
 9| search domain_abuse=true 
10| table message_id, src_user, email_domain, recipients, firstTime, lastTime 
11| `monitor_email_for_brand_abuse_filter`

Data Source

Name	Platform	Sourcetype	Source	Supported App
N/A	N/A	N/A	N/A	N/A

Macros Used

Name	Value
security_content_ctime	`convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$)`
monitor_email_for_brand_abuse_filter	`search *`

monitor_email_for_brand_abuse_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_CM

Cis18Value.CIS_13

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

You need to ingest email header data. Specifically the sender's address (src_user) must be populated. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for.

Known False Positives

None at this time

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: 3