ID | Technique | Tactic |
---|
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.
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
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 |
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 |
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