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.

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The following analytic identifies suspicious DNS TXT records using a pre-trained deep learning model. It leverages DNS response data from the Network Resolution data model, categorizing TXT records into known types via regular expressions. Records that do not match known patterns are flagged as suspicious. This activity is significant as DNS TXT records can be used for data exfiltration or command-and-control communication. If confirmed malicious, attackers could use these records to covertly transfer data or receive instructions, posing a severe threat to network security.

  • Type: Anomaly
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Network_Resolution
  • Last Updated: 2024-05-13
  • Author: Abhinav Mishra, Kumar Sharad and Namratha Sreekanta, Splunk
  • ID: 92f65c3a-968c-11ed-a1eb-0242ac120002




ID Technique Tactic
T1568.002 Domain Generation Algorithms Command And Control
Kill Chain Phase
  • Command and Control
  • DE.AE
  • CIS 13
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Resolution where DNS.message_type=response AND DNS.record_type=TXT by DNS.src DNS.dest DNS.answer DNS.record_type 
| `drop_dm_object_name("DNS")` 
| rename answer as text 
| fields firstTime, lastTime, message_type,record_type,src,dest, text 
| apply detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl 
| rename predicted_is_unknown as is_suspicious_score 
| where is_suspicious_score > 0.5 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| table src,dest,text,record_type, firstTime, lastTime,is_suspicious_score 
| `detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl_filter`


The SPL above uses the following Macros:

:information_source: detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl_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.src
  • DNS.dest
  • DNS.answer

How To Implement

Steps to deploy detect suspicious DNS TXT records model into Splunk App DSDL. This detection depends on the Splunk app for Data Science and Deep Learning which can be found here - https://splunkbase.splunk.com/app/4607/ and the Network Resolution datamodel which can be found here - https://splunkbase.splunk.com/app/1621/. The detection uses a pre-trained deep learning model that needs to be deployed in DSDL app. Follow the steps for deployment here - https://github.com/splunk/security_content/wiki/How-to-deploy-pre-trained-Deep-Learning-models-for-ESCU.

  • Download the artifacts .tar.gz file from the link - https://seal.splunkresearch.com/detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.tar.gz.
  • Download the detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.ipynb Jupyter notebook from https://github.com/splunk/security_content/notebooks.
  • Login to the Jupyter Lab assigned for detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl container. This container should be listed on Containers page for DSDL app.
  • Below steps need to be followed inside Jupyter lab.
  • Upload the detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.tar.gz file into app/model/data path using the upload option in the jupyter notebook.
  • Untar the artifact detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.tar.gz using tar -xf app/model/data/detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.tar.gz -C app/model/data.
  • Upload detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.ipynb` into Jupyter lab notebooks folder using the upload option in Jupyter lab.
  • Save the notebook using the save option in Jupyter notebook.
  • Upload detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.json into notebooks/data folder.

    Known False Positives

    False positives may be present if DNS TXT record contents are similar to benign DNS TXT record contents.

Associated Analytic Story


Risk Score Impact Confidence Message
45.0 50 90 A suspicious DNS TXT response was detected on host $src$ , kindly review.

: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.


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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