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Description

This search allows you to identify DNS requests and compute the standard deviation on the length of the names being resolved, then filter on two times the standard deviation to show you those queries that are unusually large for your environment.

  • Type: Anomaly
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Network_Resolution
  • Last Updated: 2021-10-06
  • Author: Bhavin Patel, Splunk
  • ID: 1a67f15a-f4ff-4170-84e9-08cf6f75d6f5

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1048.003 Exfiltration Over Unencrypted Non-C2 Protocol Exfiltration
T1048 Exfiltration Over Alternative Protocol Exfiltration
Kill Chain Phase
  • Command & Control
NIST
  • PR.PT
  • DE.AE
  • DE.CM
CIS20
  • CIS 8
  • CIS 12
CVE
1
2
3
4
5
6
7
8
9
10
11
12
13
| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where NOT DNS.message_type IN("Pointer","PTR") by DNS.query 
| `drop_dm_object_name("DNS")` 
| eval tlds=split(query,".") 
| eval tld=mvindex(tlds,-1) 
| eval tld_len=len(tld) 
| search tld_len<=24 
| eval query_length = len(query) 
| table query query_length record_type count 
| eventstats stdev(query_length) AS stdev avg(query_length) AS avg p50(query_length) AS p50
| where query_length>(avg+stdev*2) 
| eval z_score=(query_length-avg)/stdev 
| `dns_query_length_with_high_standard_deviation_filter`

Macros

The SPL above uses the following Macros:

:information_source: dns_query_length_with_high_standard_deviation_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.query

How To Implement

To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model.

Known False Positives

It's possible there can be long domain names that are legitimate.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
56.0 70 80 A dns query $query$ with 2 time standard deviation of name len of the dns query in host $host$

: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: 4