:warning: THIS IS A EXPERIMENTAL DETECTION

This detection has been marked experimental by the Splunk Threat Research team. This means we have not been able to test, simulate, or build datasets for this detection. Use at your own risk. This analytic is NOT supported.

Try in Splunk Security Cloud

Description

The search is used to identify attempts to use your DNS Infrastructure for DDoS purposes via a DNS amplification attack leveraging ANY queries.

  • Type: Anomaly
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Network_Resolution
  • Last Updated: 2017-09-20
  • Author: Bhavin Patel, Splunk
  • ID: 8fa891f7-a533-4b3c-af85-5aa2e7c1f1eb

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1498 Network Denial of Service Impact
T1498.002 Reflection Amplification Impact
Kill Chain Phase
  • Actions On Objectives
NIST
  • DE.AE
CIS20
  • CIS 13
CVE 


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| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" "DNS.record_type"="ANY" by "DNS.dest" 
| `drop_dm_object_name("DNS")` 
| where count>200 
| `large_volume_of_dns_any_queries_filter`

Macros

The SPL above uses the following Macros:

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

Required fields

List of fields required to use this analytic.

  • _time
  • DNS.message_type
  • DNS.record_type
  • DNS.dest

How To Implement

To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model.

Known False Positives

Legitimate ANY requests may trigger this search, however it is unusual to see a large volume of them under typical circumstances. You may modify the threshold in the search to better suit your environment.

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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