Large Volume of DNS ANY Queries
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.
Description
The following analytic identifies a large volume of DNS ANY queries, which may indicate a DNS amplification attack. It leverages the Network_Resolution data model to count DNS queries of type "ANY" directed to specific destinations. This activity is significant because DNS amplification attacks can overwhelm network resources, leading to Denial of Service (DoS) conditions. If confirmed malicious, this activity could disrupt services, degrade network performance, and potentially be part of a larger Distributed Denial of Service (DDoS) attack, impacting the availability of critical infrastructure.
- Type: Anomaly
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Network_Resolution
- Last Updated: 2024-05-15
- Author: Bhavin Patel, Splunk
- ID: 8fa891f7-a533-4b3c-af85-5aa2e7c1f1eb
Annotations
ATT&CK
Kill Chain Phase
- Actions On Objectives
NIST
- DE.AE
CIS20
- CIS 13
CVE
Search
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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:
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 |
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