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Description

This search looks for DNS requests for faux domains similar to the domains that you want to have monitored for abuse.

  • Type: TTP
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Network_Resolution
  • Last Updated: 2017-09-23
  • Author: David Dorsey, Splunk
  • ID: 24dd17b1-e2fb-4c31-878c-d4f746595bfa

Annotations

ATT&CK
Kill Chain Phase
  • Delivery
  • Actions on Objectives
NIST
CIS20
CVE
1
2
3
4
5
6
| tstats `security_content_summariesonly` values(DNS.answer) as IPs min(_time) as firstTime from datamodel=Network_Resolution by DNS.src, DNS.query 
| `drop_dm_object_name("DNS")` 
| `security_content_ctime(firstTime)`
| `brand_abuse_dns` 
| `monitor_dns_for_brand_abuse_filter`

Macros

The SPL above uses the following Macros:

:information_source: monitor_dns_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.

Required field

  • _time

How To Implement

You need to ingest data from your DNS logs. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You also need to have run the search “ESCU - DNSTwist Domain Names”, which creates the permutations of the domain that will be checked for. You also need the dnstwist custom command.

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

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