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.
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
Kill Chain Phase
- CIS 7
1 2 3 4 5 6 7 8 9 10 11 | 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`
The SPL above uses the following Macros:
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.
The SPL above uses the following Lookups:
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
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
source | version: 2