:warning: WARNING THIS IS A EXPERIMENTAL analytic

We have not been able to test, simulate, or build datasets for this object. Use at your own risk. This analytic is NOT supported.

Try in Splunk Security Cloud

Description

This search looks for emails claiming to be sent from a domain similar to one that you want to have monitored for abuse.

  • Type: TTP
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Email
  • Last Updated: 2018-01-05
  • Author: David Dorsey, Splunk
  • ID: b2ea1f38-3a3e-4b8a-9cf1-82760d86a6b8

Annotations

ATT&CK
Kill Chain Phase
  • Delivery
NIST
  • PR.IP
CIS20
  • CIS 7
CVE
1
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| 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 
| `drop_dm_object_name("All_Email")` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| eval temp=split(src_user, "@") 
| eval email_domain=mvindex(temp, 1) 
| lookup update=true brandMonitoring_lookup domain as email_domain OUTPUT domain_abuse 
| search domain_abuse=true 
| table message_id, src_user, email_domain, recipients, firstTime, lastTime 
| `monitor_email_for_brand_abuse_filter`

Macros

The SPL above uses the following Macros:

:information_source: monitor_email_for_brand_abuse_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Lookups

The SPL above uses the following Lookups:

Required field

  • _time
  • All_Email.recipient
  • All_Email.src_user
  • All_Email.message_id

How To Implement

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

RBA

Risk Score Impact Confidence Message
25.0 50 50 tbd

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

Reference

Test Dataset

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 | version: 2